We introduce AEGIS, A holistic benchmark for Evaluating forensic analysis of AI-Generated academic ImageS. Compared to existing benchmarks, AEGIS features three key advances: (1) Domain-Specific Complexity: covering seven academic categories with 39 fine-grained subtypes, exposing intrinsic forensic difficulty, where even GPT-5.1 reaches 48.80% overall performance and expert models achieve only limited localization accuracy (IoU 30.09%); (2) Diverse Forgery Simulations: modeling four prevalent academic forgery strategies across 25 generative models, with 11 yielding average forensic accuracy below 50%, showing that forensics lag behind generative advances; and (3) Multi-Dimensional Forensic Evaluation: jointly assessing detection, reasoning, and localization, revealing complementary strengths between model families, with multimodal large language models (MLLMs) at 84.74% accuracy in textual artifact recognition and expert detectors peaking at 79.54% accuracy in binary authenticity detection. By evaluating 25 leading MLLMs, nine expert models, and one unified multimodal understanding and generation model, AEGIS serves as a diagnostic testbed exposing fundamental limitations in academic image forensics.

翻译：我们提出AEGIS，一个用于评估AI生成学术图像取证的综合性基准。与现有基准相比，AEGIS具有三个关键进展：（1）领域特定复杂性：覆盖七个学术类别共39个细分子类，揭示了内在的取证难度，即使GPT-5.1的整体性能也仅为48.80%，而专家模型仅实现有限的定位精度（IoU 30.09%）；（2）多样化伪造模拟：在25个生成模型中模拟了四种常见的学术伪造策略，其中11个模型的平均取证准确率低于50%，表明取证技术落后于生成技术的进步；（3）多维取证评估：联合评估检测、推理和定位能力，揭示了不同模型家族间的互补优势——多模态大语言模型（MLLM）在文本伪影识别中准确率达84.74%，而专家模型在二元真伪检测中峰值准确率为79.54%。通过评估25个领先的MLLM、9个专家模型以及一个统一的多模态理解与生成模型，AEGIS作为诊断性测试平台，暴露了学术图像取证领域的根本性局限。