A Benchmark & Dataset for Detecting AI-Manipulated Visual Evidence in the Court System
Photographic evidence is becoming increasingly vulnerable to forms of alteration and fabrication that existing legal and technical workflows are not well equipped to evaluate. Surveillance frames, dashcam stills, and phone photographs may be used to establish presence, sequence, causation, damage, or identity, yet contemporary generative systems allow non-experts to alter or fabricate such images through ordinary prompt-based interfaces. Existing image-forensics benchmarks provide important resources for face manipulation, classical tampering, and general synthetic-image detection, but they are not organized around the forms of visual evidence submitted in courts, the localized edits that...
| Authors | Kelly McConvey, Sajad Ebrahimi, Nima Jamali et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 September 2026 |
| Last updated at source | 29 September 2026 |
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