Mar 7, 2020

Principal Component Adversarial Example

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
Yonggang ZhangDacheng Tao

Abstract

Despite having achieved excellent performance on various tasks, deep neural networks have been shown to be susceptible to adversarial examples, i.e., visual inputs crafted with structural imperceptible noise. To explain this phenomenon, previous works implicate the weak capability of the classification models and the difficulty of the classification tasks. These explanations appear to account for some of the empirical observations but lack deep insight into the intrinsic nature of adversarial examples, such as the generation method and transferability. Furthermore, previous works generate adversarial examples completely rely on a specific classifier (model). Consequently, the attack ability of adversarial examples is strongly dependent on the specific classifier. More importantly, adversarial examples cannot be generated without a trained classifier. In this paper, we raise a question: what is the real cause of the generation of adversarial examples? To answer this question, we propose a new concept, called the adversarial region, which explains the existence of adversarial examples as perturbations perpendicular to the tangent plane of the data manifold. This view yields a clear explanation of the transfer property across diff...Continue Reading

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Mentioned in this Paper

Classification
Question (Inquiry)
Internal
Structure
Neural Networks (Anatomic)
Research Study

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