Can You Trust What You See? Human and AI Detection of Synthetic Legal Evidence
A new study tests humans and advanced multimodal AI models on distinguishing real legal photos from AI-generated fakes. Results show humans average 64.8% accuracy, dropping to near chance on top generators, while AI models never misidentify real images but miss most synthetic ones. The authors advocate for a combined approach of trained human review, AI screening, and provenance infrastructure like C2PA.
[2606.07613] Can You Trust What You See? Human and AI Detection of Synthetic Legal Evidence
[Submitted on 29 May 2026]
Title:Can You Trust What You See? Human and AI Detection of Synthetic Legal Evidence
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Abstract:Visual evidence has long been treated as a reliable form of legal proof, but advances in artificial intelligence (AI) are undermining that assumption. This article asks how well humans and frontier multimodal large language models (MLLMs) can distinguish authentic evidentiary photographs from AI-generated counterparts in the object-centric scenarios typical of civil disputes. We built Synthetic Legal Evidence Detection (SLED-1400), a dataset of 200 authentic evidence images paired with 1,200 synthetic counterparts produced by six contemporary text-to-image generators across ten evidence categories. The same stimuli and response format were used in a controlled web experiment with 136 lay participants and in a standardized evaluation of four MLLMs (GPT-5.1, Gemini-3-Pro, Gemini-3-Flash, Qwen3-VL-235B). Human accuracy was 64.8% overall, and 48.5% and 51.0% on the two strongest generators (Gemini-3-Pro-Image and Flux-2-Max), indistinguishable from chance. MLLMs never misclassified an authentic image (100% specificity), but missed most synthetic outputs from the harder generators, with average MLLM detection at 5.9% on Gemini-3-Pro-Image outputs. Human and MLLM errors were largely uncorrelated, while the four MLLMs were strongly correlated with each other. Neither group is a reliable standalone authenticator. We argue that visual evidence in legal proceedings should be treated as inherently contestable, and that a workable procedural response must combine trained human review, MLLM screening, and provenance infrastructure such as C2PA Content Credentials.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as: arXiv:2606.07613 [cs.CV]
(or arXiv:2606.07613v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2606.07613
arXiv-issued DOI via DataCite
Submission history
From: Jinzhe Tan [view email] [v1] Fri, 29 May 2026 16:47:14 UTC (396 KB)
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