1 citations · 1 across the 6 of their papers we have counts for
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Same Attention, Different Truths: Put Logit-Lens over Visual Attention to Detect and Mitigate LVLM Object Hallucination
Zichuan Wang, Songlin Yang, Bo Peng +4
Large Vision-Language Models (LVLMs) often suffer from object hallucination, generating objects that are absent from the image. Prior work largely attributes this to insufficient v…
Instant Preference Alignment for Text-to-Image Diffusion Models
Yang Li, Songlin Yang, Xiaoxuan Han +4
Text-to-image (T2I) generation has greatly enhanced creative expression, yet achieving preference-aligned generation in a real-time and training-free manner remains challenging. Pr…
Probing Unlearned Diffusion Models: A Transferable Adversarial Attack Perspective
Xiaoxuan Han, Songlin Yang, Wei Wang +2
Advanced text-to-image diffusion models raise safety concerns regarding identity privacy violation, copyright infringement, and Not Safe For Work content generation. Towards this,…
Counterfactual Explanations for Face Forgery Detection via Adversarial Removal of Artifacts
Yang Li, Songlin Yang, Wei Wang +3
Highly realistic AI generated face forgeries known as deepfakes have raised serious social concerns. Although DNN-based face forgery detection models have achieved good performance…
Beyond Inserting: Learning Identity Embedding for Semantic-Fidelity Personalized Diffusion Generation
Yang Li, Songlin Yang, Wei Wang +1
Advanced diffusion-based Text-to-Image (T2I) models, such as the Stable Diffusion Model, have made significant progress in generating diverse and high-quality images using text pro…
Is It Possible to Backdoor Face Forgery Detection with Natural Triggers?
Xiaoxuan Han, Songlin Yang, Wei Wang +2
Deep neural networks have significantly improved the performance of face forgery detection models in discriminating Artificial Intelligent Generated Content (AIGC). However, their…