2 citations · 3 across the 12 of their papers we have counts for
6 papers · 1 filter
AlignVAR: Towards Globally Consistent Visual Autoregression for Image Super-Resolution
Cencen Liu, Dongyang Zhang, Wen Yin +6
Visual autoregressive (VAR) models have recently emerged as a promising alternative for image generation, offering stable training, non-iterative inference, and high-fidelity synth…
BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical World
Ji Guo, Long Zhou, Zhijin Wang +4
In recent years, deep learning-based Monocular Depth Estimation (MDE) models have been widely applied in fields such as autonomous driving and robotics. However, their vulnerabilit…
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution
Ji Guo, Xiaolei Wen, Wenbo Jiang +3
With the widespread application of super-resolution (SR) in various fields, researchers have begun to investigate its security. Previous studies have demonstrated that SR models ca…
BadRefSR: Backdoor Attacks Against Reference-based Image Super Resolution
Xue Yang, Tao Chen, Lei Guo +4
Reference-based image super-resolution (RefSR) represents a promising advancement in super-resolution (SR). In contrast to single-image super-resolution (SISR), RefSR leverages an…
Backdoor Attack Against Vision Transformers via Attention Gradient-Based Image Erosion
Ji Guo, Hongwei Li, Wenbo Jiang +1
Vision Transformers (ViTs) have outperformed traditional Convolutional Neural Networks (CNN) across various computer vision tasks. However, akin to CNN, ViTs are vulnerable to back…
One Prompt to Verify Your Models: Black-Box Text-to-Image Models Verification via Non-Transferable Adversarial Attacks
Ji Guo, Wenbo Jiang, Rui Zhang +2
Recently, various types of Text-to-Image (T2I) models have emerged (such as DALL-E and Stable Diffusion), and showing their advantages in different aspects. Therefore, some third-p…