AI Generated Image
AI-generated images are increasingly realistic, raising concerns about their misuse in misinformation and fraud. Current research focuses on developing robust detectors, often employing techniques like CLIP-based models, entropy-based methods, and Fourier analysis, to distinguish them from authentic photographs. These efforts are crucial for mitigating the societal impact of manipulated or fabricated visual content, and ongoing work emphasizes improving detector accuracy, generalization across different generative models, and understanding the underlying biases present in both generated images and detection models.
Papers
Improving Interpretability and Robustness for the Detection of AI-Generated Images
Tatiana Gaintseva, Laida Kushnareva, German Magai, Irina Piontkovskaya, Sergey Nikolenko, Martin Benning, Serguei Barannikov, Gregory Slabaugh
KI-Bilder und die Widerst\"andigkeit der Medienkonvergenz: Von prim\"arer zu sekund\"arer Intermedialit\"at?
Lukas R. A. Wilde
Disability Representations: Finding Biases in Automatic Image Generation
Yannis Tevissen
PKU-AIGIQA-4K: A Perceptual Quality Assessment Database for Both Text-to-Image and Image-to-Image AI-Generated Images
Jiquan Yuan, Fanyi Yang, Jihe Li, Xinyan Cao, Jinming Che, Jinlong Lin, Xixin Cao
G-Refine: A General Quality Refiner for Text-to-Image Generation
Chunyi Li, Haoning Wu, Hongkun Hao, Zicheng Zhang, Tengchaun Kou, Chaofeng Chen, Lei Bai, Xiaohong Liu, Weisi Lin, Guangtao Zhai
The Adversarial AI-Art: Understanding, Generation, Detection, and Benchmarking
Yuying Li, Zeyan Liu, Junyi Zhao, Liangqin Ren, Fengjun Li, Jiebo Luo, Bo Luo
RHanDS: Refining Malformed Hands for Generated Images with Decoupled Structure and Style Guidance
Chengrui Wang, Pengfei Liu, Min Zhou, Ming Zeng, Xubin Li, Tiezheng Ge, Bo zheng