Title: Defensive Tensorization: Randomized Tensor Parametrization for Robust Neural Networks. Abstract: As deep neural networks become widely adopted for solving most problems in computer vision and audio-understanding, there are rising concerns about their potential vulnerability. In particular, they are very sensitive to adversarial attacks, which manipulate the input to alter models' predictions. Despite large bodies of work to address this issue, the problem remains open.  In this paper, we propose defensive tensorization, a novel adversarial defense technique that leverages a latent high order factorization of the network. Randomization is applied in the latent subspace, therefore resulting in dense reconstructed weights, without the sparsity or perturbations typically induced by the randomization.
Our approach can be easily integrated with any arbitrary neural architecture and combined with techniques like adversarial training. We empirically demonstrate the effectiveness of our approach on standard image classification benchmarks. We further validate the generalizability of our approach across domains and low-precision architectures by considering an audio classification task and binary networks. In all cases, we demonstrate superior performance compared to prior works in the target scenario.