5 papers
Osmosis Distillation: Model Hijacking with the Fewest Samples
Yuchen Shi, Huajie Chen, Heng Xu +6
Transfer learning is devised to leverage knowledge from pre-trained models to solve new tasks with limited data and computational resources. Meanwhile, dataset distillation has eme…
Turning Black Box into White Box: Dataset Distillation Leaks
Huajie Chen, Tianqing Zhu, Yuchen Zhong +7
Dataset distillation compresses a large real dataset into a small synthetic one, enabling models trained on the synthetic data to achieve performance comparable to those trained on…
MIRROR: Manifold Ideal Reference ReconstructOR for Generalizable AI-Generated Image Detection
Ruiqi Liu, Manni Cui, Ziheng Qin +12
High-fidelity generative models have narrowed the perceptual gap between synthetic and real images, posing serious threats to media security. Most existing AI-generated image (AIGI…
Beyond Artifacts: Real-Centric Envelope Modeling for Reliable AI-Generated Image Detection
Ruiqi Liu, Yi Han, Zhengbo Zhang +9
The rapid progress of generative models has intensified the need for reliable and robust detection under real-world conditions. However, existing detectors often overfit to generat…
DINO-Detect: A Simple yet Effective Framework for Blur-Robust AI-Generated Image Detection
Jialiang Shen, Jiyang Zheng, Yunqi Xue +8
With growing concerns over image authenticity and digital safety, the field of AI-generated image (AIGI) detection has progressed rapidly. Yet, most AIGI detectors still struggle u…