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Found 7 papers, 1 papers with code
Tracing the Origin of Adversarial Attack for Forensic Investigation and Deterrence
In this paper, we take the role of investigators who want to trace the attack and identify the source, that is, the particular model which the adversarial examples are generated from.
A Survey on Reinforcement Learning Security with Application to Autonomous Driving
Reinforcement learning allows machines to learn from their own experience.
Mitigating Adversarial Attacks by Distributing Different Copies to Different Users
We point out that by distributing different copies of the model to different buyers, we can mitigate the attack such that adversarial samples found on one copy would not work on another copy.
Backdoor Pre-trained Models Can Transfer to All
However, a pre-trained model with backdoor can be a severe threat to the applications.
Thief, Beware of What Get You There: Towards Understanding Model Extraction Attack
We find the effectiveness of existing techniques significantly affected by the absence of pre-trained models.
A-FMI: Learning Attributions from Deep Networks via Feature Map Importance
Gradient-based attribution methods can aid in the understanding of convolutional neural networks (CNNs).
Where Does the Robustness Come from? A Study of the Transformation-based Ensemble Defence
This paper aims to provide a thorough study on the effectiveness of the transformation-based ensemble defence for image classification and its reasons.
