Deep Learning Model Security: Threats and Defenses

• URL: http://arxiv.org/abs/2412.08969v2
• Date: Mon, 16 Dec 2024 03:34:55 GMT
• Title: Deep Learning Model Security: Threats and Defenses
• Authors: Tianyang Wang, Ziqian Bi, Yichao Zhang, Ming Liu, Weiche Hsieh, Pohsun Feng, Lawrence K. Q. Yan, Yizhu Wen, Benji Peng, Junyu Liu, Keyu Chen, Sen Zhang, Ming Li, Chuanqi Jiang, Xinyuan Song, Junjie Yang, Bowen Jing, Jintao Ren, Junhao Song, Hong-Ming Tseng, Silin Chen, Yunze Wang, Chia Xin Liang, Jiawei Xu, Xuanhe Pan, Jinlang Wang, Qian Niu,
• Abstract summary: Deep learning has transformed AI applications but faces critical security challenges. This survey examines these vulnerabilities, detailing their mechanisms and impact on model integrity and confidentiality. The survey concludes with future directions, emphasizing automated defenses, zero-trust architectures, and the security challenges of large AI models.
• Score: 25.074630770554105
• License:
• Abstract: Deep learning has transformed AI applications but faces critical security challenges, including adversarial attacks, data poisoning, model theft, and privacy leakage. This survey examines these vulnerabilities, detailing their mechanisms and impact on model integrity and confidentiality. Practical implementations, including adversarial examples, label flipping, and backdoor attacks, are explored alongside defenses such as adversarial training, differential privacy, and federated learning, highlighting their strengths and limitations. Advanced methods like contrastive and self-supervised learning are presented for enhancing robustness. The survey concludes with future directions, emphasizing automated defenses, zero-trust architectures, and the security challenges of large AI models. A balanced approach to performance and security is essential for developing reliable deep learning systems.

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