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Found 9 papers, 3 papers with code
SSL-OTA: Unveiling Backdoor Threats in Self-Supervised Learning for Object Detection
The extensive adoption of Self-supervised learning (SSL) has led to an increased security threat from backdoor attacks.
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).
What can Discriminator do? Towards Box-free Ownership Verification of Generative Adversarial Network
In recent decades, Generative Adversarial Network (GAN) and its variants have achieved unprecedented success in image synthesis.
Efficient and Low Overhead Website Fingerprinting Attacks and Defenses based on TCP/IP Traffic
To achieve a configurable trade-off between the defense and the network overhead, we further improve the list-based defense by a traffic splitting mechanism, which can combat the mentioned attacks as well as save a considerable amount of network overhead.
Cluster-guided Contrastive Graph Clustering Network
Then, guided by the high-confidence clustering information, we carefully select and construct the positive samples from the same high-confidence cluster in two views.
What can Discriminator do? Towards Box-free Ownership Verification of Generative Adversarial Networks
In recent decades, Generative Adversarial Network (GAN) and its variants have achieved unprecedented success in image synthesis.
FedCom: A Byzantine-Robust Local Model Aggregation Rule Using Data Commitment for Federated Learning
The results demonstrate its effectiveness and superior performance compared to the state-of-the-art Byzantine-robust schemes in defending against typical data poisoning and model poisoning attacks under practical Non-IID data distributions.
