A Benchmark & Dataset for Detecting AI-Manipulated Visual Evidence in the Court System

2026-09-29

AI Summary

This study addresses the challenge of detecting AI-manipulated visual evidence in judicial contexts and the lack of domain-specific benchmarks. To this end, it introduces the first courtroom-oriented benchmark and dataset for detecting AI-manipulated images. By integrating generative forgery techniques, image forensic analysis, and structured metadata annotation, this work establishes a novel evaluation framework encompassing multi-source evidence modalities, localized edits, and consumer-grade tool threat models. The authors release 1,505 samples, source code, and baseline models as open-source resources. Experimental evaluations reveal significant performance deficiencies of existing detectors in judicial evidence verification. Ultimately, this research provides critical support for advancing visual forgery detection within legal scenarios.

Abstract

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 can change what an exhibit appears to prove, or the consumer-tool threat model now facing the justice system. We introduce the CIFAR Synthetic Evidence Corpus for Detecting AI-Manipulated Images, a benchmark for evidentiary image authentication in court and justice-system contexts. The corpus contains 1,505 photographic items, including 720 authentic controls and 785 manipulated or fabricated images, spanning surveillance, dashcam, and consumer-photo imagery. Manipulations are organized into scene-condition edits, localized element edits, and full fabrications produced with contemporary generative systems. Each item is released with structured metadata covering source provenance, manipulation tier, subtype, generator, prompt template, and scene attributes, enabling controlled evaluation beyond aggregate binary detection. We also establish baselines with publicly available image-manipulation detectors, showing that current systems exhibit error profiles that remain problematic for evidentiary use. The dataset, prompts, metadata, code, and baseline evaluation scripts are released to support research on visual evidence authentication, information integrity, and trustworthy AI for the justice system.

Problem

• AI-manipulated visual evidence
• image forensics
• court system
• evidentiary image authentication
• generative AI

Innovation

• AI-manipulated evidence detection
• image forensics benchmark
• legal visual authentication
• generative image tampering
• structured metadata corpus