The Challenges and Implications of AI-Generated Deepfakes as Forged Evidence in Criminal Trials

Introduction

             The pace of advancement in artificial intelligence (AI) and particularly generative adversarial networks (GANs), has created disruptive technologies that compromise decades of understood evidentiary challenges in criminal law. Among the most alarming are deepfakes—AI-manipulated audio and video data that creates the illusion that someone said or did something, or, in some cases, creates an entirely fictitious scene. What once was a fun novelty to do on TikTok has turned into something much more insidious, a threat to the fundamental aspect of the criminal justice system: evidence. As online accessibility to such programs increases, it becomes ever more probable that false evidence will be presented in a criminal courtroom—intentionally or not. 

Traditionally, a court can adjust to various new evidentiary challenges. Whether it's the challenge of a photo being manipulated in the first half of the twentieth century or DNA testing in a late twentieth century rape case, the court weighs the credibility for or against admission of such evidence. However, where deepfakes are concerned, they not only create a compromised situation, but also raise doubt against credible photos and videos. Thus, the threat is not just wrongful conviction or acquittal; it threatens the respect for the judicial process itself by taking away people's ability to trust the truth of what they see and hear. 

This paper proposes that deepfakes in the realm of criminal trials are overwhelmingly risky. It explores the technology behind deepfakes, their positive potentials and their legal implications when it comes to ethical, socio-political and national security matters to determine that deepfakes without reform in law and technology are a risk to evidentiary reliability and thereby, justice. 

Current Use of AI and Deepfakes 

Disruptive innovations have emerged regarding information generation, dissemination and verification with the advent of artificial intelligence. Perhaps one of the most consequential new programs associated with AI is the deepfake. The deepfake is a hyper-realistic digital fabrication rendered with machine learning processes, including generative adversarial networks (GANs). Deepfakes utilize algorithms trained on extensive collections of audio or visual data to recreate someone's face, voice, or movements with startling precision. Although they first surfaced as a legitimate technology to enhance creative ideas, the trajectory of deepfakes now falls under complicated, and sometimes malicious, applications. 

Applications and Early Uses 

Initially, deepfake technology was a product of entertainment and media. Film studios and advertisers utilized the technology to add visualizations, bring dead actors back to life, and even create multilingual dubbing. According to Westerlund (2019), deepfakes were used in the beginning as innovative digital creative tools, a means of a more artistic approach to storytelling and narrative addition. Kietzmann and Pitt (2020) add that corporate marketers sought to use deepfake-styled personalization in advertisements, using AI to synthesize the voice of a customer or implementing different content for different demographics in the same commercial. Therefore, it is easy to see how a dual-use technology may emerge and one that enhances creative expression now foreshadows misconduct in the future. 

Malicious Exploitation 

Yet the more nefarious side of deepfakes has emerged. Kietzmann and Pitt (2020) cite real life events of synthetic voices used for fraudulent financial gain, such as a situation in 2021 when a UK energy company pleaded with their banks about a wire scam worth hundreds of thousands because someone had their CEO's voice on the line. Chesney and Citron (2019) expand this concern into the realm of the “liar’s dividend” whereby the existence of deepfakes renders any video or audio untrustworthy. This assessment against the counterfeit world operates to give a two-for-one punch to bad actors: first, they can create fabricated evidence without repercussion; second, when genuine recordings exist against a guilty party, they can easily denounce it as fake. The liar's dividend is one of the largest concerns facing criminal trials today. While individual fraud is one concern, a concern for society and a wider scale of media rendition exists 

Deepfakes in Misinformation and Public Trust 

Deepfakes increasingly contribute to disinformation as well. According to Vaccari and Chadwick (2020), findings indicate that exposure to the information that a video is fake, even after exposing audiences to the fraud, results in decreased trust in legitimate media, as well. This reduction in trust relative to audiovisual communications is problematic across numerous settings. For example, in a courtroom, where video or audio is assessed and traditionally highly persuasive, exposure to deepfakes could undermine what content is ever used as evidence for either legal or regulatory purposes. Accordingly, Maras and Alexandrou (2019) note that deepfakes increasingly help blur the distinction between legitimate and disinformation recordings, with no courts currently left with reliable mechanisms for factual determination. 

Impact on Legal Proceedings 

The emerging threat of deepfakes casts a novel danger over criminal trials where real recordings may not be trusted, and fake recordings can never be disproven with such ease. According to the International Review of Law and Jurisprudence (n.d.), current rules of evidence, including those relating to authentication of digital materials, will fail in the face of AI-enhanced tampering. Forensic scientists traditionally use digital metadata, eye witnessed confirmation, or obvious disjuncts to authenticate; however, the more professional-grade digital manipulation becomes, the more limited these means of authentication are. Furthermore, the risk is elevated due to institutional weaknesses in digital evidence acquisition and maintenance, where altered files are admitted into evidence and no one is the wiser. 

Broader Social and Institutional Implications 

But even beyond financial fraud and courtroom exploitation, deepfakes threaten societal faith in institutions. Brundage et al. (2018) caution against the larger, nefarious potential of artificial intelligence in general. For example, they predict that disinformation campaigns might be used to erratically steer political governance. Their prediction extends to how synthesized media might be used against something as reliable as government watchdogs and courts to enforce a lack of faith in legitimacy. If people believe any video or audio evidence to be fake news, then actual evidence is questionable. Even with this new form of evidence for each individual court case, the overall appreciation for democratic governance and rule of law becomes threatened. 

Summary of Benefits and Risks 

Where deepfakes are concerned, there is still potential for ethical benefits in the world of education, accessibility and creative fields, but in terms of criminal justice, the advantages no longer outweigh the risks. By facilitating new realities, confusing juries and muddling standards of admissible evidence, deepfakes become a new technological risk to justice. Where once they may have been a gift of creative advancement, the movement toward unethical use champions a call to action for courts, legislators and technology creators alike. 

Legal, Ethical, and Social Implications 

The emergence of deepfakes as potential evidence in criminal trials poses interconnected legal, ethical, and social challenges. These challenges extend beyond individual cases to touch on the credibility of entire judicial systems, as courts grapple with technologies that undermine long-standing evidentiary standards. 

Legal Challenges 

The primary issue from an evidentiary perspective is reliability. Courts have typically had equity rules on what constitutes reliable evidence that need to be authenticated. Yet deepfake technology is such that an element of media production or creation may go undetected by conventional means. Maras and Alexandrou (2019) note that means of authentication like expert testimony or analysis of the metadata required to establish evidence credibility are becoming less effective against high-end forgeries. The International Review of Law and Jurisprudence (n.d.) also asserts that many legal systems do not possess remedial guidelines on determining admissibility for disputed audio and visual evidence. Thus, a lacuna exists in the rules of evidence where deepfakes can either slip through the cracks or legitimate evidence can be cast aside. 

Deepfakes also raise criminal liability questions. Should the intentional creation or use of deepfakes in legal proceedings constitute a standalone offense, akin to perjury or obstruction of justice? Chesney and Citron (2019) argue for legal reform, noting that existing frameworks often fail to address the malicious introduction of synthetic evidence. Some jurisdictions, such as California, have begun criminalizing certain deepfake uses (e.g., election interference or nonconsensual pornography), but comprehensive legislation covering courtroom misuse remains limited. 

Ethical Challenges 

The ethical issues surrounding deepfakes stem from their potential to exacerbate injustice and harm. At the individual level, deepfakes can result in wrongful convictions or acquittals, violating principles of fairness and due process. Innocent individuals may be falsely implicated through fabricated media, while guilty parties may exploit the "liar’s dividend" to dismiss incriminating evidence (Chesney & Citron, 2019). Such outcomes erode the ethical foundation of the legal system, which is grounded in truth and justice. 

Increased AI systems play into the equity dynamics of deepfakes. Trained data sets are not always diverse enough for systems to accurately discern manipulations involving marginalized persons. According to Maras and Alexandrou (2019), detection tools that are flawed will yield inconsistent results across races or genders, compounding inequities established through the structure of the criminal justice system. Here, ethics surround regulations of fairness, responsibility, and accountability of AI developments when used as evidence toward establishing guilt or innocence. 

In addition to collateral damage comes the ethical framing of harm based upon what is seen. If a deepfake falsely impliculates someone in a crime or staged violence, the reputation of the afflicted party will suffer psychological and social harm that cannot be easily reversed. Even if one is found not guilty due to such a deepfake, the stigma of being caught on film for such a crime lingers. According to Westerlund (2019), this is especially problematic since what happens on the internet stays on the internet. 

Social Challenges 

On a social scale, deepfakes undermine institutional trust. Vaccari and Chadwick (2020) note that exposure to deepfakes lower levels of trust in reputable media sources. This level of distrust, extended to the judicial system, means that people will have less faith in the courts' ability to determine the truth from a lie. Should juries, judges, or even the public lose faith in any and all audio-visual pieces of evidence, then one of the most compelling integrity-preserving resources used in trials will be rendered ineffective. 

Brundage et al. (2018) further caution that deepfakes may be weaponized to destabilize political and judicial institutions, contributing to a broader “post-truth” environment. In this context, deepfakes are not just a courtroom issue but a societal one, where the erosion of trust in evidence undermines democracy itself. Moreover, the unequal distribution of technological literacy means that some populations may be more vulnerable to manipulation than others, raising concerns about accessibility and social inequality. 

Security Aspects 

The security concerns surrounding deepfakes stem not only from their ability to deceive but also from the difficulty of detecting them with certainty. In criminal trials, where audiovisual evidence can be decisive, these challenges carry profound implications for due process and the integrity of justice. 

Forensic Authentication Challenges 

Traditional forensic methods for authenticating digital evidence—such as examining metadata, pixel inconsistencies, or compression artifacts—are increasingly insufficient against high-quality deepfakes. As Maras and Alexandrou (2019) note, forensic experts once relied on visible flaws like mismatched lighting or irregular frame rates, but newer generative adversarial networks are capable of producing videos with few, if any, detectable anomalies. This sophistication undermines the ability of courts to rely on expert testimony or conventional tools to distinguish between authentic and manipulated files. 

The problem is compounded by the fact that many detection systems are reactive rather than proactive. According to the 2024 IEEE Access study, AI detection tools often require access to the original source file and extensive training data in order to identify subtle discrepancies. In practice, however, courts may only have access to circulated or compressed versions of a video, which reduces the effectiveness of these forensic methods. This lag in detection capabilities illustrates what Brundage et al. (2018) describe as the "security arms race" between creators and detectors of malicious AI. 

Chain of Custody Vulnerabilities 

Beyond detection, deepfakes highlight vulnerabilities in the chain of custody for digital evidence. At every point between collection and presentation, there exists an opportunity for files to be intercepted, altered, or substituted. The International Review of Law and Jurisprudence (n.d.) emphasizes that many legal systems lack robust mechanisms to guarantee the integrity of digital evidence throughout this process. Without cryptographic safeguards or blockchain-based verification, courts may find it impossible to prove that a piece of video or audio has not been tampered with. 

Emerging solutions, such as embedding cryptographic hashes or tamper-proof digital watermarks at the time of recording, show promise. For example, initiatives like Adobe’s Content Authenticity Initiative and Microsoft’s Project Origin aim to attach secure provenance data to audiovisual materials. Yet, as Westerlund (2019) notes, widespread adoption of these tools remains slow, particularly in under-resourced forensic labs and courts. The uneven availability of such infrastructure risks creating disparities across jurisdictions. 

Undermining Trust in Audiovisual Evidence 

Perhaps the most insidious security challenge is not technical but psychological. Judges and juries are conditioned to trust what they can see and hear, but deepfakes exploit this intuition. A fabricated video showing a defendant committing a crime can be extraordinarily persuasive. At the same time, the existence of deepfakes makes it easy for guilty parties to dismiss genuine recordings as fabrications, a dynamic Chesney and Citron (2019) capture in the concept of the “liar’s dividend.” In this sense, deepfakes threaten to destabilize one of the pillars of evidentiary security: confidence in audiovisual materials as reliable truth. 

The Ongoing Arms Race 

The broader security landscape is characterized by an ongoing arms race between deepfake generators and detection technologies. Brundage et al. (2018) argue that this dynamic mirrors earlier challenges in cybersecurity, where defenders must constantly adapt to new offensive capabilities. While government agencies and private firms such as DARPA, Microsoft, and Google have invested heavily in deepfake detection, progress remains fragmented. According to the 2024 IEEE Access study, even the most advanced detection systems struggle to keep pace with rapidly improving generative models. Until detection tools are both reliable and widely available, the risk of courtroom misuse remains acute. 

Future Use and Legal Countermeasures 

             Given the rapidly evolving nature of deepfake technology, a multi-pronged approach combining technological, legal, and procedural measures is essential. On the technological front, advanced machine learning models are being developed to detect inconsistencies in eye movements, facial micro-expressions, and audio-visual mismatches. Tools such as Microsoft’s Video Authenticator and Google’s FaceForensics++ dataset represent early steps in this direction, though their courtroom adoption remains limited and dependent on continuous updates and rigorous testing (IEEE Access, 2024). 

Legal reforms are equally critical. Courts must adapt by implementing mandatory provenance tracking for all digital evidence, requiring expert authentication for contested audiovisual material, and providing training for judges and attorneys on AI-generated content (International Review of Law and Jurisprudence, n.d.; Chesney & Citron, 2019). Legislators may also consider criminalizing the intentional creation and submission of deepfake evidence, following precedents established in jurisdictions such as California and the European Union. 

To further strengthen evidence integrity, embedding tamper-proof digital watermarks or cryptographic hashes at the time of recording can serve as a robust chain-of-custody tool. Initiatives like Adobe’s Content Authenticity Initiative and Microsoft and BBC’s Project Origin aim to ensure that manipulations of digital content are detectable, offering courts an added layer of verification (Westerlund, 2019). 

Finally, safeguarding the integrity of criminal trials requires cross-disciplinary collaboration. Technologists, legal scholars, lawmakers, and forensic experts must work together to create ethical frameworks, develop best practices, and guide policy decisions. Universities, think tanks, and policy centers can serve as hubs for research, training, and advisory functions, helping the justice system stay ahead of emerging threats. 

 

Conclusion 

One of the greatest dangers to truth exists within the digital age, deepfakes. Using deepfake technology to create highly realistic and misleading audio and videos recordings, the presumption of innocence, the importance of proof, and the foundations of criminal justice are all at risk. 

Therefore, from a legal perspective, deepfakes are a threat that requires changes in hearsay and other evidentiary rules and new legislation to create criminal penalties for nefarious actors. From an ethical perspective, deepfakes endanger social justice and equity because they are inherently unfair and disproportionately disadvantage vulnerable populations. From a safety perspective, deepfakes are something where preventative expenditures need to be made, including means of authentication, traceable authentication, and interdisciplinary efforts. 

Where deepfakes have positive elements for movies, accessibility efforts, and educational pursuits, the negative aspects as applied in criminal courts overshadow any positive impact. Ultimately, there needs to be a marriage between evolution and fairness; otherwise, absent judicial and legislative forays, even the criminal court as a system risk becoming the victim of this proactive technology. 

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