IEEE Consumer Electronics Magazine

Adversarial Examples in CE:

Computer Security & Cryptography Artificial Intelligence

Artificial Intelligence (AI) has found extensive applications in various domains of Consumer Electronics (CE), including FinTech, smart homes, autonomous driving, information security, and so on. It encompasses various data types such as voice, Natural Language Processing (NLP), images, videos, wireless radio-frequency signals, and more. AI excels in feature extraction, prediction, and recognition across these diverse data types. In essence, humanity has become inseparable from AI. In this context, attackers have shifted their focus to the core of AI, creating Adversarial Examples (ADV) that escape human perception with the intention of deceiving AI models. These versatile ADVs can be concealed within various data types. The development of such attacks poses a significant threat to humanity, particularly in a scenario where our dependence on AI continues to grow. By delving into ADV attack techniques, prevention methods, and mitigation strategies in the realm of CEs, this special issue aims to provide valuable insights and knowledge for IEEE Consumer Electronics Magazine readers. This special issue is dedicated to AI security and privacy of CE hardware and software systems. We welcome submissions on various aspects, including attacks design, predictions, and preventive measures related to ADVs in CE across different domains. Topics of interest include but are not limited to:

1.    Identification and Defense Against ADVs in Biometric Payment Systems (e.g., fingerprint, voiceprint, facial recognition) within FinTech CE.

2.    Impact of ADVs on Autonomous Driving Safety and Mitigation Strategies.

3.    ADVs in Fake News Detection.

4.    Identification of Counterfeit Radio-Frequency Base Stations Based on ADVs.

5.    Advancements in ADV Techniques Using Various AI/ML and Metaheuristic Algorithms.

6.    Exploring Explainable AI (XAI) in the Context of ADVs.

7.    Novel ADV Attack Designs in the AI-SPC Domain.

8.    Adversarial Training Techniques in AI-SPC.

9.    ADV Attacks and Defenses in Reinforcement Learning or Federated Learning.

10.    Value-Added Applications of ADV-Related Research in Various CE Domains.