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Found 6 papers, 0 papers with code
Scalable Backdoor Detection in Neural Networks
Recently, it has been shown that deep learning models are vulnerable to Trojan attacks, where an attacker can install a backdoor during training time to make the resultant model misidentify samples contaminated with a small trigger patch.
Semantic Host-free Trojan Attack
In this paper, we propose a novel host-free Trojan attack with triggers that are fixed in the semantic space but not necessarily in the pixel space.
Towards Effective and Robust Neural Trojan Defenses via Input Filtering
Trojan attacks on deep neural networks are both dangerous and surreptitious.
Defense Against Multi-target Trojan Attacks
To defend against this attack, we first introduce a trigger reverse-engineering mechanism that uses multiple images to recover a variety of potential triggers.
Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation
Since the EMA generator can be considered as an ensemble of the generator's old versions and often undergoes a smaller change in updates compared to the generator, training on its synthetic samples can help the student recall the past knowledge and prevent the student from adapting too quickly to new updates of the generator.
Revisiting the Dataset Bias Problem from a Statistical Perspective
To overcome this challenge, we propose to approximate \frac{1}{p(u|b)} using a biased classifier trained with "bias amplification" losses.
