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Enhancing the transferability of adversarial attacks via Scale Enriching
Yuhang Zhao1, Jun Zheng1, Xianfeng Gao1
1School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, 100081, China.
Summary
Adversarial attacks on deep learning models can be improved using the Scale Enriching Method (SEM). SEM enhances adversarial example transferability across models with different input sizes, boosting attack success rates.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Deep learning models are susceptible to adversarial attacks.
- Transfer-based adversarial examples often lack transferability across models with varying input sizes in black-box settings.
Purpose of the Study:
- To propose the Scale Enriching Method (SEM) for enhancing adversarial example transferability.
- To improve the effectiveness of adversarial attacks against models with different input sizes.
Main Methods:
- Developed an input scale-enriching framework (SEM).
- Scaled surrogate model inputs within a specific range to enrich attention areas.
- Preserved inherent textural features by avoiding extraneous noise during perturbation generation.
Main Results:
- Significantly improved transferability of adversarial examples between models with different input sizes.
- Mitigated the transferability gap on ImageNet experiments.
- Achieved over 90% success rate in bypassing various defense mechanisms.
- Demonstrated successful integration with existing adversarial attack methods.
Conclusions:
- SEM effectively enhances adversarial example transferability, particularly for models with differing input sizes.
- The method preserves image features while improving attack robustness and success rates.
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