180 citations · 951 across the 62 of their papers we have counts for
11 papers · 1 filter
Evading DeepFake Detectors via Adversarial Statistical Consistency
Yang Hou, Qing Guo, Yihao Huang +3
In recent years, as various realistic face forgery techniques known as DeepFake improves by leaps and bounds,more and more DeepFake detection techniques have been proposed. These m…
Learning to Adversarially Blur Visual Object Tracking
Qing Guo, Ziyi Cheng, Felix Juefei-Xu +4
Motion blur caused by the moving of the object or camera during the exposure can be a key challenge for visual object tracking, affecting tracking accuracy significantly. In this w…
EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining
Qing Guo, Jingyang Sun, Felix Juefei-Xu +4
Single-image deraining is rather challenging due to the unknown rain model. Existing methods often make specific assumptions of the rain model, which can hardly cover many diverse…
Adversarial Rain Attack and Defensive Deraining for DNN Perception
Liming Zhai, Felix Juefei-Xu, Qing Guo +5
Rain often poses inevitable threats to deep neural network (DNN) based perception systems, and a comprehensive investigation of the potential risks of the rain to DNNs is of great…
Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack
Yupeng Cheng, Qing Guo, Felix Juefei-Xu +4
Image denoising can remove natural noise that widely exists in images captured by multimedia devices due to low-quality imaging sensors, unstable image transmission processes, or l…
DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms
Hua Qi, Qing Guo, Felix Juefei-Xu +5
As the GAN-based face image and video generation techniques, widely known as DeepFakes, have become more and more matured and realistic, there comes a pressing and urgent demand fo…
FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction
Yihao Huang, Felix Juefei-Xu, Run Wang +7
At this moment, GAN-based image generation methods are still imperfect, whose upsampling design has limitations in leaving some certain artifact patterns in the synthesized image.…
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks
Qing Guo, Felix Juefei-Xu, Xiaofei Xie +5
The state-of-the-art deep neural networks (DNNs) are vulnerable against adversarial examples with additive random-like noise perturbations. While such examples are hardly found in…
FakeLocator: Robust Localization of GAN-Based Face Manipulations
Yihao Huang, Felix Juefei-Xu, Qing Guo +2
Full face synthesis and partial face manipulation by virtue of the generative adversarial networks (GANs) and its variants have raised wide public concerns. In the multi-media fore…
Amora: Black-box Adversarial Morphing Attack
Run Wang, Felix Juefei-Xu, Qing Guo +4
Nowadays, digital facial content manipulation has become ubiquitous and realistic with the success of generative adversarial networks (GANs), making face recognition (FR) systems s…
SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking
Qing Guo, Xiaofei Xie, Felix Juefei-Xu +5
Adversarial attacks of deep neural networks have been intensively studied on image, audio, natural language, patch, and pixel classification tasks. Nevertheless, as a typical, whil…