Adversarial Examples: Opportunities and Challenges

• Published in: IEEE transaction on neural networks and learning systems Vol. 31; no. 7; pp. 2578 - 2593
• Main Authors: Zhang, Jiliang, Li, Chen
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adversarial examples (AEs); Artificial intelligence; artificial intelligence (AI); Artificial neural networks; Biological neural networks; deep neural networks (DNNs); Machine learning; Neural networks; Neurons; Object recognition; Perturbation methods; Robots; Security; Speech processing; Speech recognition; Training
• ISSN: 2162-237X; 2162-2388; 2162-2388
• DOI: 10.1109/TNNLS.2019.2933524

Deep neural networks (DNNs) have shown huge superiority over humans in image recognition, speech processing, autonomous vehicles, and medical diagnosis. However, recent studies indicate that DNNs are vulnerable to adversarial examples (AEs), which are designed by attackers to fool deep learning models. Different from real examples, AEs can mislead the model to predict incorrect outputs while hardly be distinguished by human eyes, therefore threaten security-critical deep-learning applications. In recent years, the generation and defense of AEs have become a research hotspot in the field of artificial intelligence (AI) security. This article reviews the latest research progress of AEs. First, we introduce the concept, cause, characteristics, and evaluation metrics of AEs, then give a survey on the state-of-the-art AE generation methods with the discussion of advantages and disadvantages. After that, we review the existing defenses and discuss their limitations. Finally, future research opportunities and challenges on AEs are prospected.