Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models

• URL: http://arxiv.org/abs/2504.04747v1
• Date: Mon, 07 Apr 2025 05:41:35 GMT
• Title: Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models
• Authors: Yoojin Jung, Byung Cheol Song,
• Abstract summary: We introduce EED, which diversifies the compression of a single base model based on different pruning importance scores and enhances ensemble diversity to achieve high adversarial robustness and resource efficiency.<n>EED demonstrated state-of-the-art performance compared to existing adversarial pruning techniques, along with an inference speed improvement of up to 1.86 times.
• Score: 21.88436406884943
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deep learning-based computer vision systems adopt complex and large architectures to improve performance, yet they face challenges in deployment on resource-constrained mobile and edge devices. To address this issue, model compression techniques such as pruning, quantization, and matrix factorization have been proposed; however, these compressed models are often highly vulnerable to adversarial attacks. We introduce the \textbf{Efficient Ensemble Defense (EED)} technique, which diversifies the compression of a single base model based on different pruning importance scores and enhances ensemble diversity to achieve high adversarial robustness and resource efficiency. EED dynamically determines the number of necessary sub-models during the inference stage, minimizing unnecessary computations while maintaining high robustness. On the CIFAR-10 and SVHN datasets, EED demonstrated state-of-the-art robustness performance compared to existing adversarial pruning techniques, along with an inference speed improvement of up to 1.86 times. This proves that EED is a powerful defense solution in resource-constrained environments.

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