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Found 4 papers, 1 papers with code
A Desynchronization-Based Countermeasure Against Side-Channel Analysis of Neural Networks
We analyze the timing properties of several activation functions and design the desynchronization in a way that the dependency on the input and the activation type is hidden.
FooBaR: Fault Fooling Backdoor Attack on Neural Network Training
In particular, we discuss attacks against ReLU activation functions that make it possible to generate a family of malicious inputs, which are called fooling inputs, to be used at inference time to induce controlled misclassifications.
SNIFF: Reverse Engineering of Neural Networks with Fault Attacks
In this paper we explore the possibility to reverse engineer neural networks with the usage of fault attacks.
DeepLaser: Practical Fault Attack on Deep Neural Networks
As deep learning systems are widely adopted in safety- and security-critical applications, such as autonomous vehicles, banking systems, etc., malicious faults and attacks become a tremendous concern, which potentially could lead to catastrophic consequences.
