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Found 17 papers, 6 papers with code
A Survey of Defenses against AI-generated Visual Media: Detection, Disruption, and Authentication
Deep generative models have demonstrated impressive performance in various computer vision applications, including image synthesis, video generation, and medical analysis.
A Survey of Deep Learning Library Testing Methods
This paper provides an overview of the testing research related to various DL libraries, discusses the strengths and weaknesses of existing methods, and provides guidance and reference for the application of the DL library.
Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Driving
Deep learning-based monocular depth estimation (MDE), extensively applied in autonomous driving, is known to be vulnerable to adversarial attacks.
Adversarial Example Soups: Improving Transferability and Stealthiness for Free
Transferable adversarial examples cause practical security risks since they can mislead a target model without knowing its internal knowledge.
Collapse-Aware Triplet Decoupling for Adversarially Robust Image Retrieval
Furthermore, CA prevents the consequential model collapse, based on a novel metric, collapseness, which is incorporated into the optimization of perturbation.
Towards Deep Learning Models Resistant to Transfer-based Adversarial Attacks via Data-centric Robust Learning
Instead, a one-shot adversarial augmentation prior to training is sufficient, and we name this new defense paradigm Data-centric Robust Learning (DRL).
Exploiting Facial Relationships and Feature Aggregation for Multi-Face Forgery Detection
Face forgery techniques have emerged as a forefront concern, and numerous detection approaches have been proposed to address this challenge.
GhostEncoder: Stealthy Backdoor Attacks with Dynamic Triggers to Pre-trained Encoders in Self-supervised Learning
Pre-trained image encoders can serve as feature extractors, facilitating the construction of downstream classifiers for various tasks.
SSL-Auth: An Authentication Framework by Fragile Watermarking for Pre-trained Encoders in Self-supervised Learning
Self-supervised learning (SSL), a paradigm harnessing unlabeled datasets to train robust encoders, has recently witnessed substantial success.
Hard Adversarial Example Mining for Improving Robust Fairness
Adversarial training (AT) is widely considered the state-of-the-art technique for improving the robustness of deep neural networks (DNNs) against adversarial examples (AE).
End-to-end Face-swapping via Adaptive Latent Representation Learning
Taking full advantage of the excellent performance of StyleGAN, style transfer-based face swapping methods have been extensively investigated recently.
Artificial Intelligence Security Competition (AISC)
The security of artificial intelligence (AI) is an important research area towards safe, reliable, and trustworthy AI systems.
Towards Benchmarking and Evaluating Deepfake Detection
Deepfake detection automatically recognizes the manipulated medias through the analysis of the difference between manipulated and non-altered videos.
Spatial Dual-Modality Graph Reasoning for Key Information Extraction
In order to roundly evaluate our proposed method as well as boost the future research, we release a new dataset named WildReceipt, which is collected and annotated tailored for the evaluation of key information extraction from document images of unseen templates in the wild.
RobustScanner: Dynamically Enhancing Positional Clues for Robust Text Recognition
Theoretically, our proposed method, dubbed \emph{RobustScanner}, decodes individual characters with dynamic ratio between context and positional clues, and utilizes more positional ones when the decoding sequences with scarce context, and thus is robust and practical.
Can We Mitigate Backdoor Attack Using Adversarial Detection Methods?
It is unknown whether there are any connections and common characteristics between the defenses against these two attacks.
Object Instance Mining for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD) using only image-level annotations has attracted growing attention over the past few years.
Ranked #16 on
Weakly Supervised Object Detection
on PASCAL VOC 2007
