Related papers: On the Computational Entanglement of Distant Features in Adversarial Machine Learning

On the Computational Entanglement of Distant Features in Adversarial Machine Learning

URL: http://arxiv.org/abs/2309.15669v7
Date: Mon, 30 Sep 2024 05:58:19 GMT
Title: On the Computational Entanglement of Distant Features in Adversarial Machine Learning
Authors: YenLung Lai, Xingbo Dong, Zhe Jin,
Abstract summary: We introduce the concept of "computational entanglement" Computational entanglement enables the network to achieve zero loss by fitting random noise, even on previously unseen test samples. We present a novel application of computational entanglement in transforming a worst-case adversarial examples-inputs that are highly non-robust.
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Abstract: In this research, we introduce the concept of "computational entanglement," a phenomenon observed in overparameterized feedforward linear networks that enables the network to achieve zero loss by fitting random noise, even on previously unseen test samples. Analyzing this behavior through spacetime diagrams reveals its connection to length contraction, where both training and test samples converge toward a shared normalized point within a flat Riemannian manifold. Moreover, we present a novel application of computational entanglement in transforming a worst-case adversarial examples-inputs that are highly non-robust and uninterpretable to human observers-into outputs that are both recognizable and robust. This provides new insights into the behavior of non-robust features in adversarial example generation, underscoring the critical role of computational entanglement in enhancing model robustness and advancing our understanding of neural networks in adversarial contexts.

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