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AI in Forensics: Between Technological Revolution and Human Challenges

Articles, Forensic police, News, Scientific progress, ,

By Yann CHOVORY, Engineer in AI Applied to Criminalistics (Institut Génétique Nantes Atlantique – IGNA). On a crime scene, every minute counts. Between identifying a fleeing suspect, preventing further wrongdoing, and managing the time constraints of an investigation, case handlers are engaged in a genuine race against the clock. Fingerprints, gunshot residues, biological traces, video surveillance, digital data… all these clues must be collected and quickly analyzed, or there is a risk that the case will collapse for lack of usable evidence in time. Yet overwhelmed by the ever-growing mass of data, forensic laboratories are struggling to keep pace.

Analyzing evidence with speed and accuracy

In this context, artificial intelligence (AI) establishes itself as an indispensable accelerator. Capable of processing in a few hours what would take weeks to analyze manually, it optimises the use of clues by speeding up their sorting and detecting links imperceptible to the human eye. More than just a time-saver, it also improves the relevance of investigations: swiftly cross-referencing databases, spotting hidden patterns in phone call records, comparing DNA fragments with unmatched precision. AI thus acts as a tireless virtual analyst, reducing the risk of human error and offering new opportunities to forensic experts.

But this technological revolution does not come without friction. Between institutional scepticism and operational resistance, its integration into investigative practices remains a challenge. My professional journey, marked by a persistent quest to integrate AI into scientific policing, illustrates this transformation—and the obstacles it faces. From a marginalised bioinformatician to project lead for AI at IGNA, I have observed from within how this discipline, long grounded in traditional methods, is adapting—sometimes under pressure—to the era of big data.

The risk of human error is reduced and the reliability of identifications increased

Concrete examples: AI from the crime scene to the laboratory

AI is already making inroads in several areas of criminalistics, with promising results. For example, AFIS (Automated Fingerprint Identification System) fingerprint recognition systems now incorporate machine learning components to improve matching of latent fingerprints. The risk of human error is reduced and the reliability of identifications increased [1]. Likewise, in ballistics, computer vision algorithms now automatically compare the striations on a projectile with markings of known firearms, speeding the work of a firearms expert. Tools are also emerging to interpret bloodstains on a scene: machine learning1 models can help reconstruct the trajectory of blood droplets and thus the dynamics of an assault or violent event [2]. These examples illustrate how AI is integrating into the forensic expert’s toolkit, from crime scene image analysis to the recognition of complex patterns.But it is perhaps in forensic genetics that AI currently raises the greatest hopes. DNA analysis labs process thousands of genetic profiles and samples, with deadlines that can be critical. AI offers a considerable time-gain and enhanced accuracy. As part of my research, I contributed to developing an in-house AI capable of interpreting 86 genetic profiles in just three minutes [3]—a major advance when analyzing a complex profile may take hours. Since 2024, it has autonomously handled simple profiles, while complex genetic profiles are automatically routed to a human expert, ensuring effective collaboration between automation and expertise. The results observed are very encouraging. Not only is the turnaround time for DNA results drastically reduced, but the error rate also falls thanks to the standardization introduced by the algorithm.

AI does not replace humans but complements them

Another promising advance lies in enhancing genetic DNA-based facial composites. Currently, this technique allows estimating certain physical features of an individual (such as eye color, hair color, or skin pigmentation) from their genetic code, but it remains limited by the complexity of genetic interactions and uncertainties in predictions. AI could revolutionise this approach by using deep learning models trained on vast genetic and phenotypic databases, thereby refining these predictions and generating more accurate sketches. Unlike classical methods, which rely on statistical probabilities, an AI model could analyse millions of genetic variants in a few seconds and identify subtle correlations that traditional approaches do not detect. This prospect opens the way to a significant improvement in the relevance of DNA sketches, facilitating suspect identification when no other usable clues are available. The Forenseek platform has explored current advances in this area, but AI has not yet been fully exploited to surpass existing methods [5]. Its integration could therefore constitute a major breakthrough in criminal investigations.

It is important to emphasize that in all these examples, AI does not replace the human but complements them. At IRCGN (French National Gendarmerie Criminal Research Institute) cited above, while the majority of routine, good-quality DNA profiles can be handled automatically, regular human quality control remains: every week, a technician randomly checks cases processed by AI, to ensure no drift has occurred [3]. This human-machine collaboration is key to successful deployment, as the expertise of the forensic specialists remains indispensable to validate and finely interpret the results, especially in complex cases.

Intelligence artificielle IA en police scientifique et cybercriminalité - Forenseek

Algorithms Trained on Data: How AI “Learns” in Forensics

The impressive performance of AI in forensics relies on one crucial resource: data. For a machine learning algorithm to identify a fingerprint or interpret a DNA profile, it first needs to be trained on numerous examples. In practical terms, we provide it with representative datasets, each containing inputs (images, signals, genetic profiles, etc.) associated with an expected outcome (the identity of the correct suspect, the exact composition of the DNA profile, etc.). By analyzing thousands—or even millions—of these examples, the machine adjusts its internal parameters to best replicate the decisions made by human experts. This is known as supervised learning, since the AI learns from cases where the correct outcome is already known. For example, to train a model to recognize DNA profiles, we use data from solved cases where the expected result is clearly established.

an AI’s performance depends on the quality of the data that trains it.

The larger and more diverse the training dataset, the better the AI will be at detecting reliable and robust patterns. However, not all data is equal. It must be of high quality (e.g., properly labeled images, DNA profiles free from input errors) and cover a wide enough range of situations. If the system is biased by being exposed to only a narrow range of cases, it may fail when confronted with a slightly different scenario. In genetics, for instance, this means including profiles from various ethnic backgrounds, varying degrees of degradation, and complex mixture configurations so the algorithm can learn to handle all potential sources of variation.

Transparency in data composition is essential. Studies have shown that some forensic databases are demographically unbalanced—for example, the U.S. CODIS database contains an overrepresentation of profiles from African-American individuals compared to other groups [6]. A model naively trained on such data could inherit systemic biases and produce less reliable or less fair results for underrepresented populations. It is therefore crucial to monitor training data for bias and, if necessary, to correct it (e.g., through balanced sampling, augmentation of minority data) in order to achieve fair and equitable learning.

Data Collection: Gathering diverse and representative datasets
Data Preprocessing: Cleaning and preparing data for training
AI Training: Training algorithms on prepared datasets
Data Validation: Verifying the quality and diversity of the data
Bias Evaluation: Identifying and correcting biases in the datasets

Technically, training an AI involves rigorous steps of cross-validation and performance measurement. We generally split data into three sets: one for training, another for validation during development (to adjust the parameters), and a final test set to objectively evaluate the model. Quantitative metrics such as accuracy, recall (sensitivity), or error curves make it possible to quantify how reliable the algorithm is on data it has never seen [6]. For example, one can check that the AI correctly identifies a large majority of perpetrators from traces while maintaining a low rate of false positives. Increasingly, we also integrate fairness and ethical criteria into these evaluations: performance is examined across demographic groups or testing conditions (gender, age, etc.), to ensure that no unacceptable bias remains [6]. Finally, compliance with legal constraints (such as the GDPR in Europe, which regulates the use of personal data) must be built in from the design phase of the system [6]. That may involve anonymizing data, limiting certain sensitive information, or providing procedures in case an ethical bias is detected.

Ultimately, an AI’s performance depends on the quality of the data that trains it. In the forensic field, that means algorithms “learn” from accumulated human expertise. Every algorithmic decision implies the experience of hundreds of experts who provided examples or tuned parameters. It is both a strength – capitalizing on a vast knowledge base – and a responsibility: to carefully select, prepare, and control the data that will feed the artificial intelligence.

Technical and operational challenges for integrating AI into forensic science

Technical and operational challenges for integrating AI into forensic science

While AI promises substantial gains, its concrete integration in the forensic field faces many challenges. It is not enough to train a model in a laboratory: one must also be able to use it within the constrained framework of a judicial investigation, with all the reliability requirements that entails. Among the main technical and organisational challenges are:

  • Access to data and infrastructure: Paradoxically, although AI requires large datasets to learn, it can be difficult to gather sufficient data in the specific forensic domain. DNA profiles, for example, are highly sensitive personal data, protected by law and stored in secure, sequestered databases. Obtaining datasets large enough to train an algorithm may require complex cooperation between agencies or the generation of synthetic data to fill gaps. Additionally, computing tools must be capable of processing large volumes of data in reasonable time — which requires investment in hardware (servers, GPU2s for deep learning3) and specialized software. Some national initiatives are beginning to emerge to pool forensic data securely, but this remains an ongoing project.
  • Quality of annotations and bias: The effectiveness of AI learning depends on the quality of the annotations in training datasets. In many forensic areas, establishing « ground truth » is not trivial. For example, to train an algorithm to recognize a face in surveillance video, each face must be correctly identified by a human first — which can be difficult if the image is blurry or partial. Similarly, labeling data sets of footprints, fibers, or fingerprints requires meticulous work by experts and sometimes involves subjectivity. If the training data include annotation errors or historical biases, the AI will reproduce them [6]. A common bias is demographic representativeness noted above, but there may be others. For instance, if a weapon detection model is trained mainly on images of weapons indoors, it may perform poorly for detecting a weapon outdoors, in rain, etc. The quality and diversity of annotated data are therefore a major technical issue. This implicates establishing rigorous data collection and annotation protocols (ideally standardized at the international level), as well as ongoing monitoring to detect model drift (overfitting to certain cases, performance degradation over time, etc.). This validation relies on experimental studies comparing AI performance to that of human experts. However, the complexity of homologation procedures and procurement often slows adoption, delaying the deployment of new tools in forensic science by several years.
Intelligence Artificielle IA en police scientifique et en sciences forensiques cybercriminalité - Forenseek
  • Understanding and Acceptance by Judicial Actors: Introducing artificial intelligence into the judicial process inevitably raises the question of trust. An investigator or a laboratory technician, trained in conventional methods, must learn to use and interpret the results provided by AI. This requires training and a gradual cultural shift so that the tool becomes an ally and not an “incomprehensible black box.” More broadly, judges, attorneys, and jurors who will have to discuss this evidence must also grasp its principles. Yet explaining the inner workings of a neural network or the statistical meaning of a similarity score is far from simple. We sometimes observe misunderstanding or suspicion from certain judicial actors toward these algorithmic methods [6]. If a judge does not understand how a conclusion was reached, they may be inclined to reject it or assign it less weight, out of caution. Similarly, a defence lawyer will legitimately scrutinize the weaknesses of a tool they do not know, which may lead to judicial debates over the validity of the AI. A major challenge is thus to make AI explainable (the “XAI” concept—eXplainable Artificial Intelligence), or at least to present its results in a comprehensible format and pedagogically acceptable to a court. Without this, integrating AI risks facing resistance or sparking controversy in trials, limiting its practical contribution.
  • Regulatory Framework and Data Protection: Finally, forensic sciences operate within a strict legal framework, notably regarding personal data (DNA profiles, biometric data, etc.) and criminal procedure. The use of AI must comply with these regulations. In France, the CNIL (Commission Nationale de l’Informatique et des Libertés) keeps watch and can impose restrictions if an algorithmic processing harms privacy. For example, training an AI on nominal DNA profiles without a legal basis would be inconceivable. Innovation must therefore remain within legal boundaries, imposing constraints from the design phase of projects. Another issue concerns trade secrecy surrounding certain algorithms in judicial contexts: if a vendor refuses to disclose the internal workings of its software for intellectual property reasons, how can the defence or the judge ensure its reliability? Recent cases have shown defendants convicted on the basis of proprietary software (e.g., DNA analysis) without the defence being able to examine the source code used [7]. These situations raise issues of transparency and rights of defence. In the United States, a proposed law titled Justice in Forensic Algorithms Act aims precisely to ensure that trade secrecy cannot prevent the examination by experts of the algorithms used in forensics, in order to guarantee fairness in trials. This underlines the necessity of adapting regulatory frameworks to these new technologies.

Lack of Cooperation slows the development of powerful tools and limits their adoption in the field.

  • Another more structural obstacle lies in the difficulty of integrating hybrid profiles within forensic institutions, at least in France. Today, competitive examinations and recruitment often remain compartmentalised between different specialties, limiting the emergence of experts with dual expertise. For instance, in forensic police services, entrance exams for technicians or engineers are divided into distinct specialties such as biology or computer science, without pathways to recognize combined expertise in both fields. This institutional rigidity slows the integration of professionals capable of bridging between domains and fully exploiting the potential of AI in criminalistics. Yet current technological advances show that the analysis of biological traces increasingly relies on advanced digital tools. Faced with this evolution, greater flexibility in recruitment and training of forensic experts will be necessary to meet tomorrow’s challenges.

AI in forensics must not become a matter of competition or prestige among laboratories, but a tool put at the service of justice and truth, for the benefit of investigators and victims.

  • A further major barrier to innovation in forensic science is the compartmentalization of efforts among different stakeholders, who often work in parallel on identical problems without pooling their advances. This lack of cooperation slows the development of effective tools and limits their adoption in the field. However, by sharing our resources—whether databases, methodologies, or algorithms—we could accelerate the production deployment of AI solutions and guarantee continuous improvement based on collective expertise. My experience across different French laboratories (the Lyon Scientific Police Laboratory (Service National de Police Scientifique – SNPS), the Institut de Recherche Criminelle de la Gendarmerie Nationale (IRCGN), and now the Nantes Atlantique Genetic Institute (IGNA)) allows me to perceive how much this fragmentation hampers progress, even though we pursue a common goal: improving the resolution of investigations. This is why it is essential to promote open-source development when possible and to create platforms of collaboration among public and judicial entities. AI in forensics must not be a matter of competition or prestige among laboratories, but a tool in the service of justice and truth, for the benefit of investigators and victims alike.
Intelligence Artificielle IA en police scientifique et en sciences forensiques - Forenseek

The challenges discussed above all have technical dimensions, but they are closely intertwine with fundamental ethical and legal questions. From an ethical standpoint, the absolute priority is to avoid injustice through the use of AI. We must prevent at all costs that a poorly designed algorithm leads to someone’s wrongful indictment or, conversely, the release of a guilty party. This involves mastering biases (to avoid discrimination against certain groups), transparency (so that every party in a trial can understand and challenge algorithmic evidence), and accountability for decisions. Indeed, who is responsible if an AI makes an error? The expert who misused it, the software developer, or no one because “the machine made a mistake”? This ambiguity is unacceptable in justice: it is essential to always keep human expertise in the loop, so that a final decision—whether to accuse or exonerate—is based on human evaluation informed by AI, and not on the opaque verdict of an automated system.

On the legal side, the landscape is evolving to regulate the use of AI. The European Union, in particular, is finalizing an AI Regulation (AI Act) which will be the world’s first legislation establishing a framework for the development, commercialization, and use of artificial intelligence systems [8]. Its goal is to minimize risks to safety and fundamental rights by imposing obligations depending on the level of risk of the application (and forensic or criminal justice applications will undoubtedly be categorized among the most sensitive). In France, the CNIL has published recommendations emphasizing that innovation can be reconciled with respect for individual rights during the development of AI solutions [9]. This involves, for example, compliance with the GDPR, limitation of purposes (i.e. training a model only for legitimate and clearly defined objectives), proportionality in data collection, and prior impact assessments for any system likely to significantly affect individuals. These safeguards aim to ensure that enthusiasm for AI does not come at the expense of the fundamental principles of justice and privacy.

Encouraging Innovation While Demanding Scientific Validation and Transparency

A delicate balance must therefore be struck between technological innovation and regulatory framework. On one hand, overly restricting experimentation and adoption of AI in forensics could deprive investigators of tools potentially decisive for solving complex cases. On the other, leaving the field unregulated and unchecked would risk judicial errors or violations of rights. The solution likely lies in a measured approach: encouraging innovation while demanding solid scientific validation and transparency in methods. Ethics committees and independent experts can be involved to audit algorithms, verify that they comply with norms, and that they do not replicate problematic biases. Furthermore, legal professionals must be informed and trained on these new technologies so they can meaningfully debate their probative value in court. A judge trained in the basic concepts of AI will be better placed to understand the evidentiary weight (and limitations) of evidence derived from an algorithm.

Conclusion: The Future of forensics in the AI Era

Artificial intelligence is set to deeply transform forensics, offering investigators analysis tools that are faster, more accurate, and capable of handling volumes of data once considered inaccessible. Whether it is sifting through gigabytes of digital information, comparing latent traces with improved reliability, or untangling complex DNA profiles in a matter of minutes, AI opens new horizons for solving investigations more efficiently.

But this technological leap comes with crucial challenges. Learning techniques, quality of databases, algorithmic bias, transparency of decisions, regulatory framework: these are all stakes that will determine whether AI can truly strengthen justice without undermining it. At a time when public trust in digital tools is more than ever under scrutiny, it is imperative to integrate these innovations with rigor and responsibility.The future of AI in forensics will not be a confrontation between machine and human, but a collaborative work in which human expertise remains central. Technology may help us see faster and farther, but interpretation, judgment and decision-making will remain in the hands of forensic experts and the judicial authorities. Thus, the real question may not be how far AI can go in forensic science, but how we will frame it to ensure that it guarantees ethical and equitable justice. Will we be able to harness its power while preserving the very foundations of a fair trial and the right to defence?

The revolution is underway. It is now up to us to make it progress, not drift.

Bibliography

[1]​ : Océane DUBOUST. L’IA peut-elle aider la police scientifique à trouver des similitudes dans les empreintes digitales ? Euronews, 12/01/2024 [vue le 15/03/2025] https://fr.euronews.com/next/2024/01/12/lia-peut-elle-aider-la-police-scientifique-a-trouver-des-similitudes-dans-les-empreintes-d#:~:text=,il
[2] : International Journal of Multidisciplinary Research and Publications. The Role of Artificial Intelligence in Forensic Science: Transforming Investigations through Technology. Muhammad Arjamand et al. Volume 7, Issue 5, pp. 67-70, 2024. Disponible sur : http://ijmrap.com/ [vue le 15/03/2025]
[3]​ : Gendarmerie Nationale. Kit universel, puce RFID, IA : le PJGN à la pointe de la technologie sur l’ADN.  Mis à jour le 22/01/2025 et disponible sur : https://www.gendarmerie.interieur.gouv.fr/pjgn/recherche-et-innovation/kit-universel-puce-rfid-ia-le-pjgn-a-la-pointe-de-la-technologie-sur-l-adn [vue le 15/03/2025]
[4]​ : Michelle TAYLOR. EXCLUSIVE: Brand New Deterministic Software Can Deconvolute a DNA Mixture in Seconds.  Forensic Magazine, 29/03/022. Disponible sur : https://www.forensicmag.com [vue le 15/03/2025]
[5]​ : Sébastien AGUILAR. L’ADN à l’origine des portraits-robot ! Forenseek, 05/01/2023. Disponible sur : https://www.forenseek.fr/adn-a-l-origine-des-portraits-robot/ [vue le 15/03/2025]
[6]​ : Max M. Houck, Ph.D.  CSI/AI: The Potential for Artificial Intelligence in Forensic Science.  iShine News, 29/10/2024. Disponible sur : https://www.ishinews.com/csi-ai-the-potential-for-artificial-intelligence-in-forensic-science/ [vue le 15/03/2025]
[7]​ : Mark Takano.  Black box algorithms’ use in criminal justice system tackled by bill reintroduced by reps. Takano and evans.  Takano House, 15/02/2024. Disponible sur : https://takano.house.gov/newsroom/press-releases/black-box-algorithms-use-in-criminal-justice-system-tackled-by-bill-reintroduced-by-reps-takano-and-evans [vue le 15/03/2025]
[8] : Mon Expert RGPD. Artificial Intelligence Act : La CNIL répond aux premières questions.  Disponible sur : https://monexpertrgpd.com [vue le 15/03/2025]
[9]​ ​: ​ CNIL.  Les fiches pratiques IA.  Disponible sur : https://www.cnil.fr [vue le 15/03/2025]

Définitions :

  1. GPU (Graphics Processing Unit)
    A GPU is a specialized processor designed to perform massively parallel computations. Originally developed for rendering graphics, it is now widely used in artificial intelligence applications, particularly for training deep learning models. Unlike CPUs (central processing units), which are optimized for sequential, general-purpose tasks, GPUs contain thousands of cores optimized to execute numerous operations simultaneously on large datasets
  2. Machine Learning
    Machine learning is a branch of artificial intelligence that enables computers to learn from data without being explicitly programmed. It relies on algorithms capable of detecting patterns, making predictions, and improving performance through experience.
  3. Deep Learning
    Deep learning is a subfield of machine learning that uses artificial neural networks composed of multiple layers to model complex data representations. Inspired by the human brain, it allows AI systems to learn from large volumes of data and enhance their performance over time. Deep learning is especially effective for processing images, speech, text, and complex signals, with applications in computer vision, speech recognition, forensic science, and cybersecurity.

The sexome: a potential source of evidence in sexual assault cases

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Thanks to new sampling and analytical techniques, forensic science now plays an essential role in solving sexual crimes. In cases where the search for semen fails, the sexome—also referred to as the genital microbiome—could take over and become a complementary, or even decisive, investigative tool.

What is the sexome? At a time when the importance of the human microbiota is being recognized in numerous areas of health, researchers are no longer confined to the studying of the bacterial flora colonizing the skin and the intestine. They are also focusing on the microorganisms that inhabit the male and female genital areas—the genital microbiome. Their work primarily addresses health-related questions, such as the prevention of sexually transmitted infections, but its implications extend further.

A unique microbial signature

A study conducted by a team of researchers from Murdoch University in Perth, Australia, on about a dozen heterosexual couples, demonstrated that each individual possesses a distinctive genital microbial flora. This flora, more abundant in women than in men, is transferred from one partner to another during sexual intercourse. According to Brendan Chapman, forensic scientist and co-author of the study, the discovery of these microbial “traces” could offer an effective alternative for identifying perpetrators of sexual offences.

Identification possible even in condom-protected intercourse

According to the scientists behind this discovery, this new technique could play a decisive role when semen DNA analysis proves problematic. The collection of biological material from victims of sexual assault is now highly advanced and, thanks to genetic databases, enables numerous identifications. However, this method faces several challenges, particularly related to time constraints. Beyond 48 hours, the quantity of sperm cells decreases dramatically and may no longer be sufficient for conclusive DNA analysis. Furthermore, in the absence of ejaculation or when a condom has been used, these biological traces are nonexistent.

By contrast, with the help of advanced sequencing techniques, it is possible to detect the sexual microbial signature transferred from one partner to another in samples collected up to five days after sexual contact. Even more remarkably, these transfers can still be detected after washing the genital area, and—though in smaller quantities—even when a condom has been used. In such cases, explains Brendan Chapman, it is mainly components of the female sexual microbiome that are recovered from the male genital area. This approach could help identify more sexual offenders even in the absence of DNA evidence, without requiring additional samples to be taken from already deeply traumatized victims.

The next step for scientists is to refine the technique by determining which factors can influence the sexome—particularly the vaginal microbiome, which fluctuates with the menstrual cycle—since such variations may affect the accuracy of results. This promising line of research opens new perspectives for forensic science.

Read the full study here.

DNA is also tracking down drug traffickers

News, ,

From raw product to the small plastic bag of drugs sold on the street, every stage of handling increases the chances of leaving traces behind. Whether fingerprints or biological residues (DNA), such evidence is invaluable for tracing the network back to the traffickers.

DNA — a key tool in criminal investigations

Depuis quelques années, l’analyse de l’ADN est devenue un outil incontournable pour élucider les Over the past few years, DNA analysis has become an essential tool in solving criminal cases, including long-unsolved cold cases. In a recent study conducted at Flinders University, the research team led by forensic science PhD candidate Madison Nolan and Professor Adrian Linacre proposes to push the boundaries of suspect identification in drug trafficking cases through advanced genetic profiling.

Packaging as a source of evidence

Before reaching the streets, drugs are transformed and packaged in various types of containers — which can become a goldmine of information for forensic investigators. However, repeated handling and exposure to environmental factors can degrade DNA, sometimes rendering it unusable. To support forensic work, the Flinders team focused on identifying the areas of drug packaging most likely to retain exploitable biological traces.

Better DNA transfer within the packaging

According to the study’s findings, DNA presence was particularly significant on capsules containing powdered substances and on the inner surfaces of “Ziploc”-type bags used to store them, especially along the interior edges of the seal. Even brief contact—around 30 seconds—was enough to leave detectable amounts of DNA. Because these traces are located inside the packaging, the risk of external contamination is considerably reduced.

New perspectives for forensic investigations

For forensic police, this research offers new insights for optimizing sampling during drug seizures. By focusing primarily on the outer surfaces of capsules and the inner surfaces of plastic bags, investigators can obtain higher-quality genetic profiles—provided that collection procedures are followed meticulously to avoid any contamination. Nevertheless, as the researchers caution, DNA recovered from seized materials may already be degraded by previous handling or transport conditions, which can limit its reliability.

Sources :

https://www.sciencedirect.com/science/article/pii/S1872497324001789 https://news.flinders.edu.au/blog/2025/02/03/dna-study-targets-drug-making/

De la recherche ADN aux analyses de données numériques : l’évolution fulgurante de la police scientifique

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La police scientifique n’a jamais été aussi performante qu’aujourd’hui. Grâce aux avancées technologiques, les enquêtes criminelles bénéficient désormais d’outils d’investigation pointus, permettant notamment de résoudre des affaires de meurtre, de viol, de vol à main armée ou encore de terrorisme. Dans ce contexte, le concours de la Police Scientifique revêt une importance capitale, car il permet de recruter de futurs Techniciens de Police Technique et Scientifique (TPTS), chargés d’intervenir rapidement sur les scènes de crime.

Un soutien essentiel pour la Police Judiciaire

En effet, l’expertise de la police scientifique fait gagner un temps précieux à la Police Judiciaire, que ce soit dans la gestion d’une scène de crime (homicide, assassinat, etc.) ou d’une scène de délit (vol, cambriolage, dégradations, trafic de stupéfiants). L’analyse d’indices tels que les empreintes digitales, l’ADN, les fibres, les éléments balistiques ou encore les traces numériques contribue à établir des preuves solides face aux tribunaux et permet de mieux cerner le profil des suspects.

L’importance du facteur humain

Malgré ces moyens technologiques de pointe, les effectifs de la police scientifique restent profondément humains. Chaque jour, ces professionnels doivent composer avec des situations parfois dramatiques et faire face à la détresse des victimes. Dans l’émission LEGEND sur YouTube, animée par Guillaume Pley, le policier scientifique Sébastien Aguilar, souligne l’impact psychologique de ces enquêtes. Il évoque des affaires hors du commun, parfois totalement folles, mais aussi des cas dont la violence l’a marqué pour toujours.

Police Scientifique : un métier loin des clichés

Cette réalité du terrain est souvent bien différente des clichés véhiculés au sujet de la police scientifique. Dans son ouvrage « Au cœur de l’enquête criminelle », publié dans la collection Darkside chez Hachette, Sébastien Aguilar décrit pas à pas le travail rigoureux des enquêteurs, épaulés par les policiers scientifiques entièrement dédiés à la recherche de la vérité. Il y relate également les différentes étapes qui mènent au procès en Cour d’Assises, offrant un aperçu complet du fonctionnement de la machine judiciaire.

Si vous souhaitez en savoir plus sur l’impact psychologique du métier, les techniques d’investigation modernes ou encore l’importance du concours de Technicien de Police Technique et Scientifique, retrouvez l’interview de Sébastien Aguilar par Guillaume Pley sur YouTube et plongez-vous dans « Au cœur de l’enquête criminelle » pour une immersion totale dans l’univers passionnant de la police scientifique.

cold case bientôt résolu grâce à l'ADN en parentèle Forenseek Police scientifique

Two cold cases solved through familial DNA searching ?

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As in the Élodie Kulik case in 2011 and that of the “Prédateur des bois” in 2022, two casefiles reopened by the Cold Case Unit in Nanterre are now close to being solved thanks to familial DNA searching,

“They say you’re only betrayed by your own,” a proverb that takes on its full meaning here. Two murders committed twelve years apart and seemingly unrelated have now been traced back to one and the same suspect—thanks to a genetic link established between members of the same family.

In 1988, fifteen-year-old Valérie Boyer was found with her throat slit along the railway tracks in Saint-Quentin-Fallavier. In 2000, forty-year-old Laïla Afif was shot dead in the head in La Verpillière. The only common factor between the two crimes was geographical proximity, as both occurred in neighboring towns in the Isère department. Lacking solid leads or similarities in the modus operandi, the investigations soon came to a standstill—until March 2024. More than twenty years later, the Cold Case Unit, created in 2022, reopened the Laïla Afif case and ordered new DNA analyses on samples recovered from the crime scene.

Proof through familial DNA 

These forensic analyses, extended through the use of familial DNA searching, led investigators to identify, within the National Automated DNA Database (Fichier National Automatisé des Empreintes Génétiques – FNAEG), an individual previously implicated in another case whose DNA showed a 50% genetic match with the profile obtained in the Afif case. And as the immutable laws of genetics dictate—each person shares half of their genome with their biological parents and children—the investigators logically traced the lead back to the man’s father. Betrayed by his son’s DNA, Mohammed C. has now been indicted not only for the murder of Laïla Afif, but also for that of Valérie Boyer, as the reopened investigation revealed striking connections between the two crimes. The latter case is part of the notorious “Disparus de l’Isère” (the Isère disappearances) series, a string of disappearances that made national headlines in the 1980s and is already under review by the Cold Case Unit.

Further reading: COLD CASES UN MAGISTRAT ENQUÊTE (Cold Cases – A Magistrate Investigates), by Jacques Dallest

Criminal history is marked by sordid murders, brutal killings, mysterious disappearances, and puzzling suicides. Mysterious and puzzling because these cases have never been solved—the perpetrators never identified, the culprits never convicted. In proper French, these cases are referred to as “cold cases.” They number in the dozens and are often unknown to the general public. Only a few major unsolved cases have found their place in the annals of judicial history and continue to fuel debate and speculation: the Bruay-en-Artois case, the Fontanet case, the Grégory case, the Boulin case, and, more recently, the Chevaline shootings. But what exactly is a cold case? What does this English term signify within the context of the French judicial system? Should these cases be reopened? And after so many years, how can justice still be served? In this scholarly and meticulously documented essay, Jacques Dallest—former investigating judge, public prosecutor, and Advocate General—offers a comprehensive analysis of the issue as no book has ever done before.

Order online.

Généalogie génétique police scientifique Forenseek

Les « biobanques », source d’info pour la police scientifique ?

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En décembre 2023, le site de généalogie génétique 23andme a subi le piratage des données ADN de presque 7 millions de clients. Une cyberattaque qui met en lumière la valeur de ces informations ultrasensibles et pas seulement pour les cybercriminels !

Il suffit désormais d’un test salivaire vendu par des sociétés spécialisées dans la généalogie génétique pour connaître ses origines et identifier ses ancêtres. Un axe ludique revendiqué par les biobanques comme 23andme, MyHeritage ou Ancestry pour ne citer qu’elles. Ces sociétés communiquent en revanche assez peu sur le risque que l’on coure à confier des données aussi confidentielles que son propre ADN, ces informations pouvant être rendue publiques ou faire l’objet d’un trafic très lucratif comme le montre ce dernier hackage informatique.

Construire son arbre généalogique génétique

Les progrès sur les analyses ADN permettent de réaliser des comparaisons de plus en plus fines entre les millions de données génétiques présentes dans ces fichiers informatiques, ces derniers étant régulièrement alimentés par des tests effectués chaque année dans le Monde. Grâce à ces comparaisons, les généalogistes génétiques peuvent retrouver des individus apparentés de façon proche ou lointaine et élaborer ainsi un véritable arbre généalogique avec à la clé, des informations sur des ancêtres souvent totalement inconnus.  Une découverte amusante et parfois déroutante sur ses origines.

L’ADN de parentèle pour résoudre les crimes

Au-delà de son aspect récréatif, la généalogie génétique est une technique qui suscite de plus en plus l’intérêt de la police scientifique. Dans ce cas, les enquêteurs confrontés à une enquête qui se trouve dans l’impasse, ne scrutent pas le passé mais se focalisent plutôt sur les branches plus contemporaines de l’arbre généalogique afin de comparer les ADN présents dans ces bases publiques avec un ADN retrouvé sur une scène de crime mais inconnu au FNAEG (Fichier National Automatisé des Empreintes Génétiques). L’objectif est de trouver une correspondance et, en partant de l’identification d’un parent plus ou moins proche, de remonter jusqu’à l’auteur de la trace biologique. Au Etats-Unis, cette technique a déjà permis d’élucider 621 affaires criminelles et en France, grâce aux recherches du FBI, d’arrêter en 2022 celui qui avait été baptisé le « prédateur des bois » à cause de son mode opératoire pour perpétrer plusieurs viols entre 1998 et 2008.  

La généalogie génétique pourrait devenir un outil d’investigation très utile pour la résolution de certains « cold case ». En revanche, en France, contrairement aux Etats-Unis où cette pratique existe déjà au grand dam de certains spécialistes des questions éthiques, cette recherche ne peut pas s’effectuer en piochant dans les bases de sociétés privées mais uniquement dans les bases génétiques créées à des fins médico-légales. Reste le réel danger du piratage des données pour lequel aucune parade n’a pour l’instant été trouvé.

Sources :

Le “prédateur des bois” mis en examen et placé en détention provisoire (francetvinfo.fr)
IGG Cases (genealogyexplained.com)
Les enjeux éthiques de l’utilisation de l’ADN dans le domaine médico-légal – Sciences et Avenir

Fingerprints: beyond identification

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Widely used by law enforcement agencies worldwide for personal identification, fingerprints can also serve as a basis to carry out various screening tests.

Interest in fingerprints has recently been reignited thanks to a new study published on February 1, 2023, by researchers at the Jasmine Breast Unit of Doncaster Royal Infirmary in the United Kingdom. The scientists have developed a digital technique capable of detecting breast cancer with an accuracy of nearly 98%.

Secretions that reveal the disease…

In this case, the focus is not on analyzing fingerprint pattern classes or minutiae—the characteristic points located along the ridge lines that allow for reliable personal identification. Instead, by taking swabs from the fingertips to collect sweat, physicians were able to detect the presence of proteins and peptides identified as biomarkers of potential breast cancer.

This non-invasive, painless technique for patients could make it possible to distinguish between benign, early-stage, or metastatic tumors. If the results are confirmed, it may soon be commercialized in the form of a kit, providing a rapid and reliable diagnostic tool that is significantly less traumatic and less costly than mammography, which remains the current gold standard in breast cancer screening.

…And also detect narcotics!

From medicine to forensic science, there is often only a small step—and in this case, technology has taken it. Sweat sampling from fingerprints is already among the available methods to detect the presence of four classes of narcotics: amphetamines, cannabis, cocaine, and opiates.

Here again, it is the sweat that reveals the presence of these molecules, whether the substance was merely handled or actually ingested. The procedure requires nothing more than pressing the fingertips onto a special piece of paper and then analyzing it using mass spectrometry, which can detect the substance up to 48 hours after contact or ingestion.

Unlike blood tests, which require significant logistics, this analytical method takes only a few minutes and can also be applied to latent fingerprint residues collected at a crime scene. It has also proven effective in the medico-legal context, using post-mortem sweat samples.

Sources :

https://www.nature.com/articles/s41598-023-29036-7

https://www.businesswire.com/news/home/20181008005386/fr/

Utilisation de l'ADN dans le portrait-robot génétique

L’ADN à l’origine des portraits-robot !

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Pour dessiner le portrait-robot d’un suspect, on avait coutume de faire appel à un portraitiste et à des logiciels spécialisés. Désormais, la police scientifique utilise aussi l’ADN récupérée sur la scène de crime pour dresser un profil physique très proche de la réalité.

En 1982, Edward Crabe, un australien de 57 ans, est assassiné dans sa chambre d’hôtel de Gold Coast situé dans l’état du Queensland. Malgré les nombreux témoignages recueillis et le prélèvement d’échantillons de sang sur la scène de crime, les enquêteurs ne réussissent pas à retrouver le coupable. 40 ans plus tard, la police australienne relance ce « cold case » en faisant appel au phénotypage de l’ADN. Cette technique encore récente, pratiquée notamment dans le laboratoire d’hématologie médico-légal de Bordeaux, permet de créer à partir de quelques cellules sanguines, le portrait-robot d’un suspect susceptible d’être identifié par les témoins ou l’entourage. 

Même inconnu, l’ADN parle !

C’est en tout cas ce qu’espèrent les policiers du Queensland qui ont lancé un nouvel appel à témoins le 9 novembre 2022. Ils comptent ainsi résoudre l’assassinat d’Edward Crabe grâce au portrait-robot établi à partir du sang retrouvé dans la chambre de la victime. Un premier profil ADN avait déjà été établi en 2020 mais celui-ci n’étant pas enregistré dans les bases de données nationales, les enquêteurs s’étaient rapidement retrouvés dans une impasse. Or, l’immense avantage du phénotypage, c’est que même lorsqu’un ADN n’est fiché nulle part, il n’en constitue pas moins une mine de renseignements sur la personne correspondante.

En quoi consiste cette technique ? On sait aujourd’hui que le matériel génétique renferme de très nombreuses informations, notamment sur le sexe, l’origine ethnique, la santé et l’apparence physique d’un individu. Les récentes techniques de séquençage de l’ADN permettent désormais d’analyser les séquences génétiques dites « codantes » et d’isoler celles qui renferment les indications morphologiques. Les scientifiques peuvent ainsi déterminer de façon suffisamment précise la forme d’un visage, la couleur de la peau, des yeux et des cheveux, une prédisposition à la calvitie ou encore la présence de taches de rousseur. A partir de ce profil génétique, il est possible d’établir un profil physique qui n’est à l’heure actuelle, ni complet, ni parfait mais qui peut permettre de réveiller les souvenirs de potentiels témoins.

Une technique qui tend à se perfectionner

Il existe aujourd’hui dans le Monde plusieurs équipes de chercheurs qui travaillent à perfectionner l’analyse de ces séquences génétiques liées au phénotype. L’objectif ?  Aller toujours plus loin dans la recherche des caractéristiques physiques des individus en prédisant par exemple la forme du lobe des oreilles ou encore l’âge de la personne étant à l’origine de la trace relevée.

Tous ces renseignements servent ensuite à mettre au point des programmes statistiques capables d’élaborer un portrait-robot génétique le plus proche possible de la réalité. Ces programmes, qui existent déjà aux États Unis, sont nourris par les entreprises proposant aux particuliers des tests ADN pour connaître leur généalogie et qui collaborent également avec les forces de l’ordre.

Dernièrement, une étude sur les sosies réalisée par une équipe de chercheurs du Leukaemia Research Institute à Barcelone (Espagne), est venue renforcer la réalité de ce portrait physique littéralement inscrit dans les gènes. En analysant l’ADN de ces « jumeaux virtuels », les scientifiques ont identifié des caractéristiques génétiques communes qui ne s’arrêtent d’ailleurs pas à  une apparence physique similaire. Elles sont également capables d’influencer certains comportements en matière d’alimentation et même d’éducation. Des résultats qui selon l’auteure principale de l’étude, Manel Esteller  «  auront des implications futures en médecine légale – reconstruire le visage du criminel à partir de l’ADN – et en diagnostic génétique – la photo du visage du patient  donnera déjà des indices sur le génome qu’il possède. Grâce à des efforts de collaboration, le défi ultime serait de prédire la structure du visage d’une personne à partir de son paysage multiomique“ .

Portraits-robot ADN génétique police scientifique aide dans la résolution des enquêtes judiciaires.
Exemple de portraits-robot génétique édités par Snapshot DNA analysis

Le projet européen VISAGE

L’objectif global du projet européen VISAGE (VISible Attributes Through GEnomics) est d’élargir l’utilisation judiciaire de l’ADN vers la construction de portraits-robot d’auteurs inconnus à partir de traces ADN le plus rapidement possible dans les cadres juridiques actuels et les lignes directrices éthiques.

Le consortium VISAGE est composé de 13 partenaires issus d’institutions universitaires, policières et judiciaires de 8 pays européens, et réunit des chercheurs en génétique légale et des praticiens en ADN judiciaire, des généticiens statistiques et des spécialistes en sciences sociales. Les objectifs sont :

  • d’établir de nouvelles connaissances scientifiques dans le domaine du phénotypage de l’ADN,
  • d’élaborer et valider de nouveaux outils dans l’analyse de l’ADN et l’interprétation statistique,
  • de valider et mettre en œuvre ces outils dans la pratique judiciaire,
  • d’étudier les dimensions éthiques, sociétales et réglementaires,
  • de diffuser largement les résultats et sensibiliser les différents protagonistes concernant la prédiction de l’apparence, de l’âge et de l’ascendance bio-géographique d’une personne à partir de traces d’ADN,
  • d’aider la justice à trouver des auteurs inconnus d’actes criminels au moyen du profilage de l’ADN

Pour aller plus loin :

https://snapshot.parabon-nanolabs.com/

Sources

https://www.newsendip.com/fr/comment-la-police-australienne-espere-resoudre-un-meurtre-de-1982-avec-un-nouveau-portrait-robot-issu-de-ladn-du-suspect/

https://www.francetvinfo.fr/faits-divers/police/enquete-quand-ladn-dessine-des-portraits-robots_4882409.html

https://www.sciencedaily.com/releases/2022/08/220823115609.htm