On the security relevance of weights in deep learning

Understand

Recently, a weight-based attack on stochastic gradient descent inducing overfitting has been proposed.

• We show that the threat is broader: A task-independent permutation on the initial weights suffices to limit the achieved accuracy to for example 50% on the Fashion MNIST dataset from initially more than $90$%.
• These findings are confirmed on MNIST and CIFAR.
• We formally confirm that the attack succeeds with high likelihood and does not depend on the data.