Mathematical Optimization Algorithms for Model Compression and Adversarial Learning in Deep Neural Networks

Large-scale deep neural networks (DNNs) have made breakthroughs in a variety of tasks, such as image recognition, speech recognition and self-driving cars. However, their large model size and computational requirements add a significant burden to state-of-the-art computing systems. Weight pruning is an effective approach to reduce the model size and computational requirements of DNNs. However, prior works in this area are mainly heuristic methods. As a result, the performance of a DNN cannot maintain for a high weight pruning ratio. To mitigate this limitation, we propose a systematic weight pruning framework for DNNs based on mathematical optimization. We first formulate the weight pruning for DNNs as a non-convex optimization problem, and then systematically solve it using alternating direction method of multipliers (ADMM). Our work achieves a higher weight pruning ratio on DNNs without accuracy loss and a higher acceleration on the inference of DNNs on CPU and GPU platforms compared with prior works. Besides the issue of model size, DNNs are also sensitive to adversarial attacks, a small invisible noise on the input data can fully mislead a DNN. Research on the robustness of DNNs follows two directions in general. The first is to enhance the robustness of DNNs, which increases the degree of difficulty for adversarial attacks to fool DNNs. The second is to design adversarial attack methods to test the robustness of DNNs. These two aspects reciprocally benefit each other towards hardening DNNs. In our work, we propose to generate adversarial attacks with low distortion via convex optimization, which achieves 100% attack success rate with lower distortion compared with prior works. We also propose a unified min-max optimization framework for the adversarial attack and defense on DNNs over multiple domains. Our proposed method performs better compared with the prior works, which use average-based strategies to solve the problems over multiple domains

Understanding and mitigating universal adversarial perturbations for computer vision neural networks

Deep neural networks (DNNs) have become the algorithm of choice for many computer vision applications. They are able to achieve human level performance in many computer vision tasks, and enable the automation and large-scale deployment of applications such as object tracking, autonomous vehicles, and medical imaging. However, DNNs expose software applications to systemic vulnerabilities in the form of Universal Adversarial Perturbations (UAPs): input perturbation attacks that can cause DNNs to make classification errors on large sets of inputs. Our aim is to improve the robustness of computer vision DNNs to UAPs without sacrificing the models' predictive performance. To this end, we increase our understanding of these vulnerabilities by investigating the visual structures and patterns commonly appearing in UAPs. We demonstrate the efficacy and pervasiveness of UAPs by showing how Procedural Noise patterns can be used to generate efficient zero-knowledge attacks for different computer vision models and tasks at minimal cost to the attacker. We then evaluate the UAP robustness of various shape and texture-biased models, and found that applying them in ensembles provides marginal improvement to robustness. To mitigate UAP attacks, we develop two novel approaches. First, we propose the Jacobian of DNNs to measure the sensitivity of computer vision DNNs. We derive theoretical bounds and provide empirical evidence that shows how a combination of Jacobian regularisation and ensemble methods allow for increased model robustness against UAPs without degrading the predictive performance of computer vision DNNs. Our results evince a robustness-accuracy trade-off against UAPs that is better than those of models trained in conventional ways. Finally, we design a detection method that analyses the hidden layer activation values to identify a variety of UAP attacks in real-time with low-latency. We show that our work outperforms existing defences under realistic time and computation constraints.Open Acces

DeepSearch: A Simple and Effective Blackbox Attack for Deep Neural Networks

Although deep neural networks have been very successful in image-classification tasks, they are prone to adversarial attacks. To generate adversarial inputs, there has emerged a wide variety of techniques, such as black- and whitebox attacks for neural networks. In this paper, we present DeepSearch, a novel fuzzing-based, query-efficient, blackbox attack for image classifiers. Despite its simplicity, DeepSearch is shown to be more effective in finding adversarial inputs than state-of-the-art blackbox approaches. DeepSearch is additionally able to generate the most subtle adversarial inputs in comparison to these approaches

Robust filtering schemes for machine learning systems to defend Adversarial Attack

Robust filtering schemes for machine learning systems to defend Adversarial Attac

Reliable and structural deep neural networks

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.Includes bibliographical references