Analysis Of Prosecution Strategies For Ai-Generated Synthetic Media In Cybercrime And Fraud
Prosecution Strategies for Synthetic Media Crimes
When dealing with AI‑generated synthetic media (videos, voice‑clips, images) used in fraud or cyber‑crime, prosecutors face certain challenges and adopt particular strategies:
Key strategic issues
Attribution and intent: The core challenge is establishing that a human actor intentionally used synthetic media to defraud or harass, rather than genuine media or innocent manipulation. Prosecutors gather evidence of the design, creation, distribution of synthetic content, and link that to human intent.
Digital forensic evidence: This involves metadata of the synthetic media (creation timestamps, editing programs, voice‑clone signatures), logs of dissemination (which accounts, how many views, where transfers occurred), tracing the money or harm that resulted from the synthetic media.
Victim reliance / harm: Prosecution must show that the victim (or target) was induced by the synthetic media to act (transfer funds, rely on endorsement, suffer reputational harm). The causal chain from synthetic media → victim response → loss is crucial.
Existing legal framework adaptation: Often existing laws (fraud statutes, impersonation/identity theft statutes, defamation, harassing communications laws) are applied. Prosecutors strategise which statute applies to the synthetic‑media tool. For example: impersonation via voice‑clone → identity theft statute; deepfake endorsement of investment scheme → fraud statute.
Scale & automation aggravation: When synthetic media allows large scale automatic dissemination (bots, automated voice‑calls, social‑media campaigns), prosecutors may argue that the synthetic media tool amplified harm and thus aggravates liability (higher penalties).
Jurisdiction & cross‑border issues: Synthetic media crimes often involve remote actors, global platforms, offshore accounts. Strategy includes tracing digital footprints, cooperating internationally, using platforms to freeze assets or preserve evidence.
Relief & prevention orders: In parallel with criminal prosecution, prosecutors or victims often seek injunctions, takedown orders, preservation orders (e.g., freeze account, require platform remove content) to prevent further dissemination of synthetic media.
Typical prosecution strategy steps
Step 1: Seizure and preservation of digital evidence: the synthetic media files, device logs, communications, financial transactions.
Step 2: Forensic analysis of the synthetic media: determining whether it is AI‑generated (voice clone, face swap, deepfake), what tool or platform might have been used, linking creation to the suspect (IP logs, user accounts).
Step 3: Linking suspect’s actions to the victim’s harm: Showing that the suspect created/distributed the synthetic media, the victim acted based on it, and there was measurable harm (transfer of money, reputational damage).
Step 4: Selecting legal charges: Based on jurisdiction, charges may include fraud, identity theft, extortion, defamation, harassment, non‑consensual sexual content, etc. Prosecutors choose the statute that best fits the synthetic‑media misuse.
Step 5: Aggregating aggravating factors: For example, that synthetic media allowed thousands of victims, used automated dissemination, impersonated a senior executive or trusted figure, caused large monetary loss or psychological harm. These factors strengthen case and increase penalties.
Step 6: Coordination with platforms & regulators: Working with social‑media platforms, intermediaries, financial institutions to trace content, freeze accounts, track transfers, issue preservation notices.
Step 7: Publicity / deterrence component: Emphasising that synthetic‑media abuse is prosecutable and that the legal system treats AI‑generated fraud seriously, to deter future offenders.
Case‑Studies
Here are four detailed cases where synthetic/AI‑generated media were central, and prosecution strategy is visible.
Case 1: Arup deepfake executive video conference scam (2024)
Facts:
A major engineering firm (Arup) was targeted via a video‑conference call apparently from a senior executive instructing a subordinate to transfer large funds. The video/audio was synthetic (deepfake) of the executive’s likeness/voice.
The employee transferred HK$200 million (~£20 m) to scammer‑controlled accounts.
The scam used AI‑generated voice/face impersonation and urgent instructions to bypass normal controls.
Prosecution (investigation) strategy highlights:
Forensic analysis of the video/voice to determine it was synthetic rather than genuine.
Tracing the funds: identifying the bank accounts, flows, layering, offshore transfers.
Examining internal control failure: why was such a large transfer allowed on the basis of a call rather than standard verification?
Using the combination of AI impersonation + financial fraud to bring charges of fraud, impersonation, money‑laundering.
Emphasising scale: large amount, high value, corporate victim, trusted executive impersonated → aggravating factor.
Coordination across jurisdictions: victim company HQ, Hong Kong bank, scammers likely operating abroad.
Implication:
This case illustrates how synthetic media enables high‑value fraud and how prosecution emphasises the chain: deepfake media → instruction → transfer → loss. It also shows the need for corporate controls to mitigate such risks.
Case 2: AI‑deepfake voice & image for investment scam of a public figure’s likeness (India)
Facts:
An influencer or public figure’s voice & likeness were cloned via AI and used in a deepfake video claiming endorsement of an “AI‑based trading platform”. The video was circulated on social media, encouraging followers to invest.
Victims transferred funds to the platform, later finding it fraudulent. The deepfake endorsement gave credibility.
The perpetrators remained partly anonymous, used fake app downloads, social‑media accounts, crypto transfers.
Prosecution strategy:
Forensic detection of cloned voice/likeness of the public figure (comparing known recordings, metadata, anomaly detection) to prove media was synthetic.
Showing the link: that the synthetic endorsement caused victims to invest and lose money → proof of causation/harm.
Charging under fraud/misrepresentation statutes: the defendants falsely represented the figure’s endorsement plus promised returns (“AI‑based trading”) knowing or recklessly failing truth.
Using personality rights/identity theft statutes: unauthorised use of public figure’s likeness for commercial gain.
Issuing injunctions/takedowns alongside criminal case: ensuring the synthetic video is removed, future misuse prevented.
Emphasising synthetic nature and large victim number as aggravating factors (scalable fraud via AI).
Implication:
This kind of case shows how synthetic media intersects with investment fraud and endorsement schemes. Prosecution strategy hinges on proving the media is fake, linking it to victim action, and applying both identity/impersonation and fraud laws.
Case 3: AI voice cloning + deepfake pornographic images of a woman (India)
Facts:
A perpetrator used AI to superimpose a woman’s face onto pornographic images (deepfake porn) and distributed them via subscription‑based adult platforms. He also created social‑media persona using the fake content, gained paid followers.
This act initially motivated by revenge, but later monetised (earning about ₹10 lakh). Victim filed complaint; police raided devices, SIM cards, bank cards.
Prosecution strategy:
Forensic seizure of devices (phones, laptops, hard disks) and analysis of synthetic media production logs, storage, editing tools, multiple fake accounts.
Use of identity theft/obscenity/defamation/harassment statutes (depending on jurisdiction) to charge creation and distribution of non‑consensual deepfake porn.
Tracing monetisation: bank cards, subscription platforms, identifying financial gain component (important for fraud/monetary‑offence dimension).
Showing victim harm: reputational damage, harassment, non‑consensual intimate image distribution.
Emphasising scale (multiple fake accounts, paid platform) and use of AI tools as aggravating factor.
Platform cooperation: obtaining logs from subscription services, social media of impersonation accounts.
Implication:
This case highlights how synthetic media is used for malicious sexual content and monetised. The prosecution strategy combines identity misuse, harassment/defamation, and financial gain.
Case 4: Former school athletics director synthesises racist/antisemitic audio recording of a principal (USA)
Facts:
A former high‑school athletics director used AI software to create a fake audio recording of the school principal making derogatory comments about Black and Jewish students. He distributed the clip widely on social media, causing outrage.
The charge was disrupting school operations; he entered an Alford plea and got four months jail.
Prosecution strategy:
Evidence collection of the AI‑generated audio clip (voice‑clone), social‑media dissemination logs, linking back to the accused.
Application of harassment/defamation and related criminal statutes (though the plea was to a disruption charge).
Using the synthetic nature of the audio as a basis for intent: the defendant intentionally manufactured the fake to damage reputation and incite reaction.
Emphasising targeted victim (principal, school community) and broader harm (racial/ethnic slur, school disruption) to justify criminal prosecution.
Cooperation with school/district, social media platforms, forensic audio specialists to show the recording was fake.
Implication:
This case shows how synthetic media is used in defamation/harassment rather than just financial fraud. Prosecution strategy still centres on linking creation and distribution of synthetic media to intent and harm, and using existing harassment laws.
Summary of Strategic Lessons
The human actor behind the AI tool is always the target of prosecution: synthetic media does not create liability by itself; intent and control are key.
Forensic evidence of synthetic media creation/dissemination is vital: metadata, editing logs, device logs, distribution logs.
Linking the synthetic media to victim harm (financial loss, reputational damage, harassment) is essential for establishing causation.
Prosecutors leverage existing statutes (fraud, identity theft, harassment, defamation, non‑consensual imagery) rather than always needing new laws—though some jurisdictions are adopting deepfake‑specific legislation.
The aggravating role of scale/automation via AI is emphasised: synthetic media allows mass distribution and faster spread, making penalties more severe.
Platform/intermediary cooperation is important: takedown orders, preservation of content, logging of accounts.
Cross‑border issues require international cooperation: synthetic media crimes often span multiple countries, platforms and financial flows.
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