Adversarial robustness in deep neural networks based on variable attributes of the stochastic ensemble model

Area of Science:

• Computer Science
• Artificial Intelligence
• Cybersecurity

Background:

• Deep neural networks (DNNs) are vulnerable to adversarial attacks, necessitating robust defense mechanisms.
• Current defense strategies often mimic cybersecurity approaches but can be passive and reactive.
• Adversarial attacks exploit vulnerabilities, leading to model failures and security risks.

Purpose of the Study:

• To propose a dynamic and stochastic ensemble strategy for enhancing DNN robustness against adversarial attacks.
• To introduce diversity and randomness into the defense mechanism to counter fixed gradient vulnerabilities.
• To improve the resilience of ensemble models against white-box attacks and reduce transferability.

Main Methods:

• Defined ensemble quantity, network structure, and smoothing parameters as variable ensemble attributes.
• Developed a stochastic ensemble strategy using heterogeneous and redundant sub-models.
• Introduced diversity and randomness to alter the input-output gradient correspondence.

Main Results:

• The proposed method demonstrated effectiveness in improving adversarial robustness, particularly against untargeted attacks.
• Experimental results on CIFAR10 showed reduced attack success rates compared to traditional ensemble models.
• The stochastic nature of the gradients made white-box attacks significantly more challenging for attackers.

Conclusions:

• The stochastic ensemble strategy offers a promising dynamic defense against adversarial attacks on DNNs.
• This approach enhances model security by increasing unpredictability and reducing vulnerability to gradient-based attacks.
• The method effectively addresses the limitations of passive defense strategies in adversarial scenarios.