Defensive Quantization: When Efficiency Meets Robustness (1904.08444v1)

Abstract: Neural network quantization is becoming an industry standard to efficiently deploy deep learning models on hardware platforms, such as CPU, GPU, TPU, and FPGAs. However, we observe that the conventional quantization approaches are vulnerable to adversarial attacks. This paper aims to raise people's awareness about the security of the quantized models, and we designed a novel quantization methodology to jointly optimize the efficiency and robustness of deep learning models. We first conduct an empirical study to show that vanilla quantization suffers more from adversarial attacks. We observe that the inferior robustness comes from the error amplification effect, where the quantization operation further enlarges the distance caused by amplified noise. Then we propose a novel Defensive Quantization (DQ) method by controlling the Lipschitz constant of the network during quantization, such that the magnitude of the adversarial noise remains non-expansive during inference. Extensive experiments on CIFAR-10 and SVHN datasets demonstrate that our new quantization method can defend neural networks against adversarial examples, and even achieves superior robustness than their full-precision counterparts while maintaining the same hardware efficiency as vanilla quantization approaches. As a by-product, DQ can also improve the accuracy of quantized models without adversarial attack.

Defensive Quantization: Navigating Efficiency and Robustness in Neural Networks

The paper "Defensive Quantization: When Efficiency Meets Robustness," authored by Ji Lin, Chuang Gan, and Song Han, addresses a critical and often overlooked challenge in neural network quantization: the susceptibility of quantized models to adversarial attacks. The paper introduces a novel quantization methodology, Defensive Quantization (DQ), to simultaneously optimize the efficiency and robustness of deep learning models, proposing a strategy to mitigate the adversarial vulnerabilities exemplified in standard quantization approaches.

Quantization and its Vulnerabilities

Quantization is a prevalent technique in deep learning for reducing the computational demands of neural networks, optimizing them for deployment on hardware accelerators such as CPUs, GPUs, TPUs, and FPGAs. While quantization is instrumental in enabling efficient inference, the paper identifies a significant trade-off—an increased susceptibility to adversarial attacks. Adversarial attacks utilize subtle, often imperceptible perturbations to input data, leading neural networks to produce erroneous outputs. In safety-critical domains like autonomous driving, such vulnerabilities represent substantial security risks.

The authors highlight that conventional quantization methods inadvertently exacerbate this vulnerability due to an "error amplification effect." As adversarial noise propagates through the network layers, it becomes increasingly amplified, pushing activations into different quantization bins and thereby compromising model robustness.

Defensive Quantization Approach

Defensive Quantization (DQ) is proposed as a countermeasure to the vulnerabilities inherent in standard quantization. The core principle of DQ lies in controlling the Lipschitz constant of the network during quantization. By constraining the Lipschitz constant, the propagation of adversarial noise can be checked, maintaining the adversarial noise within a non-expansive range during inference. This approach effectively prevents noise amplification, allowing the network to retain robustness to adversarial inputs.

Empirical Evaluation

The authors substantiate their approach through comprehensive experiments on well-known datasets such as CIFAR-10 and SVHN. The results demonstrate that Defensive Quantization not only enhances the robustness of quantized networks against adversarial examples but also matches or exceeds the robustness of full-precision models. For instance, it was observed that under adversarial attacks, DQ-maintained models achieved superior accuracy compared to their vanilla quantized counterparts. Notably, the full integration of DQ with existing adversarial defense strategies like adversarial training further bolsters resilience, indicating robust applicability as a defensive measure.

Theoretical and Practical Implications

From a theoretical standpoint, Defensive Quantization advances the understanding of the trade-offs between model efficiency and robustness. Practically, it offers a viable pathway for deploying robust and efficient models in realistic environments, where computational resources are constrained but security assurances are paramount. By ensuring that quantized models can effectively resist adversarial disruptions, DQ facilitates the safe and efficient deployment of deep learning models across various applications.

Future Developments

Looking ahead, the principles of Defensive Quantization may spur further research into integrating robustness more seamlessly into early-stage model design and training. Assessing the applicability of DQ across diverse architectures and exploring additional regularization techniques to enhance robustness without compromising efficiency may represent promising avenues for future inquiry in the field of adversarial machine learning and efficient model deployment.

In conclusion, the research presented in this paper marks an important step towards bridging the efficiency and robustness dichotomy in neural network quantization, providing a robust framework for navigating the subtle complexities associated with the deployment of deep learning models in adversarially-rich environments.