Reliable and structural deep neural networks
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Deep neural networks have dominated a wide range of computer vision research recently. However, recent studies have shown that deep neural networks are sensitive to adversarial perturbations. The limitations of deep networks cause reliability concerns in real-world problems and demonstrate that computational behaviors differ from humans. In this dissertation, we focus on investigating the characteristic of deep neural networks. The first part of this dissertation proposed an effective defense method against adversarial examples. We introduced an ensemble generative network with feedback loops, which use the feature-level denoising modules to improve the defense capability for adversarial examples. We then discussed the vulnerability of deep neural networks. We explored a consistency and sensitivity-guided attack method in a low-dimensional space, which can effectively generate adversarial examples, even in a black-box manner. Our proposed approach illustrated that the adversarial examples are transferable across different networks and universal in deep networks. The last part of this dissertation focuses on rethinking the structure and behavior of deep neural networks. Rather than enhancing defense methods against attacks, we take a further step toward developing a new structure of neural networks, which provide a dynamic link between the feature map representation and their graph-based structural representation. In addition, we introduced a new feature interaction method based on the vision transformer. The new structure can learn to dynamically select the most discriminative features and help deep networks improve the generalization ability.
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Ph. D.