Modern deep face recognition systems are crucial for identity verification and authentication in high-stakes sectors such as security, finance, and law enforcement. However, their reliability is increasingly threatened by the rapid advancement of deepfake technology. Despite considerable attention, comprehensive evaluation of how emerging deepfake generation methods affect reliability of face recognition systems remains lacking. In this paper, we introduce a principled taxonomy and a benchmarking framework that structure deepfake attacks by intent and generative mechanism, enabling consistent and comprehensive assessment. Numerous results show that diverse deepfake attacks commonly exceed 70% success and, in some regimes, surpass 90%. Built on this foundation, we investigate the underlying mechanisms across different deepfake techniques, providing not only broad empirical evaluation but also valuable insight into what drives attack success. Our in-depth analysis reveals that success is not governed by visual quality, but rather by the degree of identity controllability.

Face recognition is essential for identity authentication, but the rich visual clues in facial images pose significant privacy risks, highlighting the critical importance of privacy-preserving solutions. For instance, numerous studies have shown that generative models are capable of effectively performing reconstruction attacks that result in the restoration of original visual clues. To mitigate this threat, we introduce FracFace, a fractal-based privacy-preserving face recognition framework. This approach effectively weakens the visual clues that can be exploited by reconstruction attacks by disrupting the spatial structure in frequency domain features, while retaining the vital visual clues required for identity recognition. To achieve this, we craft a Frequency Channels Refining module that reduces sparsity in the frequency domain. It suppresses visual clues that could be exploited by reconstruction attacks, while preserving features indispensable for recognition, thus making these attacks more challenging. More significantly, we design a Frequency Fractal Mapping module that obfuscates deep representations by remapping refined frequency channels into a fractal-based privacy structure. By leveraging the self-similarity of fractals, this module preserves identity relevant features while enhancing defense capabilities, thereby improving the overall robustness of the protection scheme. Experiments conducted on multiple public face recognition benchmarks demonstrate that the proposed FracFace significantly reduces the visual recoverability of facial features, while maintaining high recognition accuracy, as well as the superiorities over state-of-the-art privacy protection approaches.

Cancellable biometrics is essential for preserving sensitive biometric information from potential exposure. Existing studies usually convert real-valued biometric vectors into protected templates by randomly generated transformation keys. However, this way is realized by the built-in functions of the cancellable biometric system, which creates vulnerabilities for cancellable biometric schemes. In this paper, we propose a novel chaos-based Index-of-Min cancellable biometric scheme, named C-IoM, for privacy-preserving template updates in biometric technique. Specifically, we first design a chaos-based cancellable biometric framework to ensure the security and privacy of the biometric template. Second, we develop a secure random chaos seed generation algorithm, which non-linearly converts the biometric vectors into protected templates and conceals biometric dimensional information. Further, we craft a sliding window selection mechanism to choose the input biometric features, allowing each feature data to fully participate in the generation of protected templates through sliding intervals. Theoretical analysis confirms that the C-IoM satisfies the criteria of irreversibility, revocability, unlinkability, and performance preservation in cancellable biometrics. Extensive experiments on LFW, CFPW, and CASIA-V5 datasets demonstrate the security of the proposed framework in protecting biometric data as well as the superiorities over state-of-the-art schemes.

Industrial cognitive radio networks (ICRNs) have been a promising spectrum -sharing solution for massive resource-constrained wireless devices in the industrial Internet of things (1IoT). However, ICRNs are raising new opportunities for ma-licious users, wherein the threat landscape is compounded with few-shot attacks due to the insufficiency of high-quality examples. In this paper, we propose a novel lightweight intrusion detection system focusing on few-shot attacks for ICRNs, called KDFS-IDS. Specifically, we first develop a teacher-student model based hierarchical intrusion detection framework for ICRNs. Second, we design a convolutional neural network-based intrusion detection model as the fundamental model for identifying few-shot attacks. Third, a knowledge distillation strategy is crafted to obtain a lightweight but sufficiently accurate model for KDFS-IDS. Extensive experiments on three public datasets demonstrate the superiorities of our proposed scheme in detecting few-shot attacks for I CRN s, in terms of both effectiveness and accuracy.