9 citations · 10 across the 9 of their papers we have counts for
10 papers
A Survey on Adversarial Attacks and Defenses for Diffusion Models Across Multiple Modalities
Ozgur Kara, Tarik Can Ozden, Furkan Horoz +6
Diffusion models have become the dominant family of generative models in the visual domain. However, their widespread public availability enables misuse at scale, motivating a rapi…
Immune2V: Image Immunization Against Dual-Stream Image-to-Video Generation
Zeqian Long, Ozgur Kara, Haotian Xue +2
Image-to-video (I2V) generation has the potential for societal harm because it enables the unauthorized animation of static images to create realistic deepfakes. While existing def…
MoGAN: Improving Motion Quality in Video Diffusion via Few-Step Motion Adversarial Post-Training
Haotian Xue, Qi Chen, Zhonghao Wang +4
Video diffusion models achieve strong frame-level fidelity but still struggle with motion coherence, dynamics and realism, often producing jitter, ghosting, or implausible dynamics…
Point-It-Out: Benchmarking Embodied Reasoning for Vision Language Models in Multi-Stage Visual Grounding
Haotian Xue, Yunhao Ge, Yu Zeng +4
Vision-Language Models (VLMs) have demonstrated impressive world knowledge across a wide range of tasks, making them promising candidates for embodied reasoning applications. Howev…
QueST: Self-Supervised Skill Abstractions for Learning Continuous Control
Atharva Mete, Haotian Xue, Albert Wilcox +2
Generalization capabilities, or rather a lack thereof, is one of the most important unsolved problems in the field of robot learning, and while several large scale efforts have set…
Diffusion Policy Attacker: Crafting Adversarial Attacks for Diffusion-based Policies
Yipu Chen, Haotian Xue, Yongxin Chen
Diffusion models (DMs) have emerged as a promising approach for behavior cloning (BC). Diffusion policies (DP) based on DMs have elevated BC performance to new heights, demonstrati…
RefDrop: Controllable Consistency in Image or Video Generation via Reference Feature Guidance
Jiaojiao Fan, Haotian Xue, Qinsheng Zhang +1
There is a rapidly growing interest in controlling consistency across multiple generated images using diffusion models. Among various methods, recent works have found that simply m…
Pixel is a Barrier: Diffusion Models Are More Adversarially Robust Than We Think
Haotian Xue, Yongxin Chen
Adversarial examples for diffusion models are widely used as solutions for safety concerns. By adding adversarial perturbations to personal images, attackers can not edit or imitat…
Toward effective protection against diffusion based mimicry through score distillation
Haotian Xue, Chumeng Liang, Xiaoyu Wu +1
While generative diffusion models excel in producing high-quality images, they can also be misused to mimic authorized images, posing a significant threat to AI systems. Efforts ha…
Diffusion-Based Adversarial Sample Generation for Improved Stealthiness and Controllability
Haotian Xue, Alexandre Araujo, Bin Hu +1
Neural networks are known to be susceptible to adversarial samples: small variations of natural examples crafted to deliberately mislead the models. While they can be easily genera…