Generative image models have recently shown significant progress in image realism, leading to public concerns about their potential misuse for document forgery. This paper explores whether contemporary open-source and publicly accessible diffusion-based generative models can produce identity document forgeries that could realistically bypass human or automated verification systems. We evaluate text-to-image and image-to-image generation pipelines using multiple publicly available generative model families, including Stable Diffusion, Qwen, Flux, Nano-Banana, and others. The findings indicate that while current generative models can simulate surface-level document aesthetics, they fail to reproduce structural and forensic authenticity. Consequently, the risk of generative identity document deepfakes achieving forensic-level authenticity may be overestimated, underscoring the value of collaboration between machine learning practitioners and document-forensics experts in realistic risk assessment.
SEED: Simple ViT and Evolving Harness for Explainable Text Forgery Detection
State Key Laboratory of Internet of Things for Smart City, Department of Computer and Information Science, University of Macau（澳门大学计算机与信息科学系智慧城市物联网国家重点实验室）；School of Information and Software Engineering, University of Electronic Science and Technology of China（电子科技大学信息与软件工程学院）
提出SEED系统，结合相似性引导数据增强、单一ViT联合检测与定位、以及基于MLLM的演化报告生成，在ACM MM 2026文本伪造挑战赛中获得第三名。
A Dual-Branch CNN for Robust Detection of AI-Generated Facial Forgeries
Computer Science Department University of Southern Maine Portland, United States（计算机科学系 俄亥俄州立大学 帕斯托市 美国）；The Department of Computer Science University of Central Oklahoma Edmond, United States（计算机科学系 中央俄克拉荷马大学 埃德蒙德 美国）
Whether, Which, and Whose: Solving the Triple Challenge of Deepfake Proactive Forensics in Multi-Face Scenarios
College of Computer Science and Technology（计算机科学与技术学院）；Xinjiang University（新疆大学）；College of Computer Science and Electronic Engineering（计算机科学与电子工程学院）；Hunan University（湖南大学）
Few-Shot Generative Model Adaption via Identity Injection and Preservation
School of Cyberspace Security, Hangzhou Dianzi University（杭州电子科技大学网络安全学院）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；School of Software Engineering, Xi’an Jiaotong University（西安交通大学软件工程学院）；Lishui University and Lishui Institute of Hangzhou Dianzi University（丽水大学和杭州电子科技大学丽水研究所）；School of Faculty of Applied Science, Macao Polytechnic University（澳门理工学院应用科学学院）；School of Communication Engineering, Hangzhou Dianzi University（杭州电子科技大学通信工程学院）
Identity Card Presentation Attack Detection: A Systematic Review
da/sec-Biometrics and Internet Security Research Group, Hochschule Darmstadt（da/sec生物识别与互联网安全研究组，达姆施塔特高等学院）；Universidad Autónoma de Manizales（自动化工学院）；Universidad de Caldas（卡利斯大学）
