Search Results for author:
Found 32 papers, 8 papers with code
Adversarial Relighting against Face Recognition
To this end, we first propose the physical model-based adversarial relighting attack (ARA) denoted as albedo-quotient-based adversarial relighting attack (AQ-ARA).
CarveNet: Carving Point-Block for Complex 3D Shape Completion
3D point cloud completion is very challenging because it heavily relies on the accurate understanding of the complex 3D shapes (e. g., high-curvature, concave/convex, and hollowed-out 3D shapes) and the unknown & diverse patterns of the partially available point clouds.
Learning to Adversarially Blur Visual Object Tracking
In this work, we explore the robustness of visual object trackers against motion blur from a new angle, i. e., adversarial blur attack (ABA).
AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning
To this end, we first comprehensively investigate two kinds of pixel denoising methods for adversarial robustness enhancement (i. e., existing additive-based and unexplored filtering-based methods) under the loss functions of image-level and semantic-level restorations, respectively, showing that pixel-wise filtering can obtain much higher image quality (e. g., higher PSNR) as well as higher robustness (e. g., higher accuracy on adversarial examples) than existing pixel-wise additive-based method.
JPGNet: Joint Predictive Filtering and Generative Network for Image Inpainting
Note that, our method as a novel framework for the image inpainting problem can benefit any existing generation-based methods.
Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization
Successful applications of InfoNCE and its variants have popularized the use of contrastive variational mutual information (MI) estimators in machine learning.
Simpler, Faster, Stronger: Breaking The log-K Curse On Contrastive Learners With FlatNCE
InfoNCE-based contrastive representation learners, such as SimCLR, have been tremendously successful in recent years.
Sparta: Spatially Attentive and Adversarially Robust Activation
In both cases, Sparta leads to CNNs with higher robustness than the vanilla ReLU, verifying the flexibility and versatility of the proposed method.
AVA: Adversarial Vignetting Attack against Visual Recognition
Moreover, we propose the geometry-aware level-set optimization method to solve the adversarial vignetting regions and physical parameters jointly.
Let There be Light: Improved Traffic Surveillance via Detail Preserving Night-to-Day Transfer
As one of the state-of-the-art perception approaches, detecting the interested objects in each frame of video surveillance is widely desired by ITS.
AdvHaze: Adversarial Haze Attack
We also visualize the correlation matrices, which inspire us to jointly apply different perturbations to improve the success rate of the attack.
DeepMix: Online Auto Data Augmentation for Robust Visual Object Tracking
Online updating of the object model via samples from historical frames is of great importance for accurate visual object tracking.
Auto-Exposure Fusion for Single-Image Shadow Removal
We conduct extensive experiments on the ISTD, ISTD+, and SRD datasets to validate our method's effectiveness and show better performance in shadow regions and comparable performance in non-shadow regions over the state-of-the-art methods.
Countering Malicious DeepFakes: Survey, Battleground, and Horizon
The creation and the manipulation of facial appearance via deep generative approaches, known as DeepFake, have achieved significant progress and promoted a wide range of benign and malicious applications.
Sparta: Spatially Attentive and Adversarially Robust Activations
Moreover, comprehensive evaluations have demonstrated two important properties of our method: First, superior transferability across DNNs.
DeepRepair: Style-Guided Repairing for DNNs in the Real-world Operational Environment
In this paper, we propose a style-guided data augmentation for repairing DNN in the operational environment.
It's Raining Cats or Dogs? Adversarial Rain Attack on DNN Perception
With this generator, we further propose the adversarial rain attack against the image classification and object detection, where the rain factors are guided by the various DNNs.
FakeRetouch: Evading DeepFakes Detection via the Guidance of Deliberate Noise
Thus we use a combination of additive noise and deep image filtering to reconstruct the fake images, and we name our method FakeRetouch.
Adversarial Exposure Attack on Diabetic Retinopathy Imagery
In this paper, we study this problem from the viewpoint of adversarial attack and identify a totally new task, i. e., adversarial exposure attack generating adversarial images by tuning image exposure to mislead the DNNs with significantly high transferability.
EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining
To fill this gap, in this paper, we regard the single-image deraining as a general image-enhancing problem and originally propose a model-free deraining method, i. e., EfficientDeRain, which is able to process a rainy image within 10~ms (i. e., around 6~ms on average), over 80 times faster than the state-of-the-art method (i. e., RCDNet), while achieving similar de-rain effects.
Bias Field Poses a Threat to DNN-based X-Ray Recognition
Our method reveals the potential threat to the DNN-based X-ray automated diagnosis and can definitely benefit the development of bias-field-robust automated diagnosis system.
Making Images Undiscoverable from Co-Saliency Detection
In this paper, we address this problem from the perspective of adversarial attacks and identify a novel task: adversarial co-saliency attack.
Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack
To this end, we initiate the very first attempt to study this problem from the perspective of adversarial attack and propose the adversarial denoise attack.
FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction
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.
DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms
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 for effective DeepFakes detectors.
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks
Besides, the attack is further enhanced by adaptively tuning the translations of object and background.
FakeLocator: Robust Localization of GAN-Based Face Manipulations
To the best of our knowledge, this is the very first attempt to solve the GAN-based fake localization problem with a gray-scale fakeness prediction map that preserves more information of fake regions.
Amora: Black-box Adversarial Morphing Attack
In this paper, we investigate and introduce a new type of adversarial attack to evade FR systems by manipulating facial content, called \textbf{\underline{a}dversarial \underline{mor}phing \underline{a}ttack} (a. k. a.
SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking
We identify that online object tracking poses two new challenges: 1) it is difficult to generate imperceptible perturbations that can transfer across frames, and 2) real-time trackers require the attack to satisfy a certain level of efficiency.
Research Commentary on Recommendations with Side Information: A Survey and Research Directions
This Research Commentary aims to provide a comprehensive and systematic survey of the recent research on recommender systems with side information.
Effects of Blur and Deblurring to Visual Object Tracking
In this paper, we address these two problems by constructing a Blurred Video Tracking benchmark, which contains a variety of videos with different levels of motion blurs, as well as ground truth tracking results for evaluating trackers.
Learning Dynamic Siamese Network for Visual Object Tracking
How to effectively learn temporal variation of target appearance, to exclude the interference of cluttered background, while maintaining real-time response, is an essential problem of visual object tracking.
Ranked #5 on
Visual Object Tracking
on OTB-2013