法庭系统中检测AI操纵视觉证据的基准与数据集
A Benchmark & Dataset for Detecting AI-Manipulated Visual Evidence in the Court System
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中文总结 AI 辅助
针对法庭视觉证据易受AI操纵的问题，提出CIFAR合成证据语料库基准，含1505个图像样本及结构化元数据，并评估现有检测器，为司法系统证据认证提供研究基础。
中文摘要 AI 辅助
摄影证据正日益容易受到现有法律和技术工作流程难以评估的篡改和伪造形式的影响。监控录像帧、行车记录仪静态图像和手机照片可用于确定在场、顺序、因果关系、损害或身份，然而当代生成系统使非专家能够通过普通的基于提示的界面来篡改或伪造此类图像。现有的图像取证基准为面部操纵、经典篡改和一般合成图像检测提供了重要资源，但它们并未围绕法庭中提交的视觉证据形式、可能改变证据展示内容的局部编辑，或司法系统现在面临的消费级工具威胁模型来组织。我们引入了CIFAR合成证据语料库，用于检测AI操纵图像，这是一个面向法庭和司法系统背景下的证据图像认证基准。该语料库包含1,505个摄影项目，包括720个真实对照和785个被操纵或伪造的图像，涵盖监控、行车记录仪和消费级照片图像。操纵被组织为场景条件编辑、局部元素编辑和由当代生成系统产生的完全伪造。每个项目都附带结构化元数据发布，涵盖来源出处、操纵层级、子类型、生成器、提示模板和场景属性，从而支持超越聚合二元检测的受控评估。我们还使用公开可用的图像操纵检测器建立了基线，表明当前系统表现出对证据使用而言仍然存在问题的错误特征。数据集、提示、元数据、代码和基线评估脚本已发布，以支持视觉证据认证、信息完整性和面向司法系统的可信AI研究。
英文摘要
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.
发表机构
- University of Toronto（多伦多大学）
- University of Waterloo（滑铁卢大学）
- University of British Columbia（不列颠哥伦比亚大学）
- University of Western Ontario（西安大略大学）
- University of Ottawa（渥太华大学）
机构由 AI 辅助整理，请以论文原文为准。
