المفاهيم الأساسية
Uniform inputs reduce activation sparsity, enabling energy-latency attacks in computer vision.
الملخص
The content explores the impact of uniform inputs on activation sparsity and energy-latency attacks in computer vision. It discusses the importance of resource efficiency in machine learning, the vulnerability of energy consumption and decision latency to attacks, and the strategies for crafting sponge examples to increase activation density. The analysis delves into the interplay of convolution, batch normalization, and ReLU activation in reducing activation sparsity. Proposed attack strategies are evaluated for their effectiveness and efficiency, showcasing the transferability of sponge examples across different neural networks. The discussion extends to potential applications for improving sparsity and efficiency in non-adversarial settings.
- Introduction to resource efficiency in deep learning
- Vulnerability of energy consumption and decision latency to attacks
- Mechanism of energy-latency attacks in reducing activation sparsity
- Proposed strategies for crafting sponge examples
- Evaluation of attack strategies and transferability across models
- Applications for improving sparsity and efficiency
الإحصائيات
"Our attacks operate at a fraction of the time that the prior methods Sponge-GA and Sponge-L-BFGS require."
"Our proposed strategies achieve a density effect which is comparable or higher than the baselines and prior work."
اقتباسات
"Resource efficiency in deep learning is not a choice but a necessity."
"Sponge examples yield narrower batch-normalization inputs close to zero for each channel."