Ultrasonic injection attacks have emerged as a critical security threat to voice-controlled devices in the Internet of Things (IoT).However, most existing attack methods are limited to single-device, fixed-direction point-to-point attacks. For the first time, this paper presents a novel ultrasonic voice injection attack method—Phased Array Attack (PAA)—which enables flexible control of the attack direction. PAA expands the traditional point-to-point attack paradigm to a point-to-area approach, allowing any devices within a 45° sector to be attacked without physically moving the transmitting apparatus. This method significantly enhances the flexibility, scalability, and extensibility of injection attacks. PAA offers three key advantages: (1) high stealth: once deployed, it can target multiple devices; (2) high compatibility: the signal modulation process is offloaded from the hardware platform to a PC, thus avoiding limitations imposed by FPGA-based implementations and enabling the use of more powerful modulation algorithms; (3) theoretical soundness: PAA employs constructive interference and nonlinear demodulation theory to enhance attack performance. To validate the feasibility and effectiveness of the proposed approach, we developed an FPGA-based prototype attack system and systematically evaluated its performance in various application scenarios. Experimental results show that the proposed method achieves a high attack success rate at distances of up to 5 meters. These findings further highlight the severe physical-layer security challenges faced by voice-controlled devices in IoT environments.

The rapid advancement of Artificial Intelligence Generated Content (AIGC) technology has enabled deepfake videos to evolve from unimodal generation to audio-visual forgeries. Existing multimodal deepfake detection methods primarily rely on capturing correlations between audio-visual modalities to improve detection performance. However, in real-world scenarios, network jitter often leads to audio-visual asynchrony, disrupting inter-modal associations and limiting the effectiveness of these methods. To address this issue, we propose a deepfake detection method specifically designed for audio-visual asynchrony scenarios. First, based on the theory of open balls in metric space, we analyze the variation mechanism of joint features in both audio-visual synchrony and asynchrony scenarios, revealing the impact of audio-visual asynchrony on detection performance. Second, we design a multimodal subspace representation module to mitigate inconsistencies in feature distributions and representation heterogeneity between modalities. We then formulate audio-visual feature alignment as an integer linear programming task and employ the Hungarian algorithm to reconstruct missing inter-modal associations. Finally, we introduce a self-supervised masked reconstruction mechanism to reconstruct missing features and construct the joint correlation matrix to measure cross-modal dependencies, enhancing the robustness of detection. Extensive experiments demonstrate that our method outperforms baselines in audio-visual asynchrony scenarios and exhibits robustness against unknown disturbances.

Current state-of-the-art plug-and-play countermeasures for mitigating adversarial examples (i.e., purification and detection) exhibit several fatal limitations, impeding their deployment in safety-critical real-world applications. These limitations include susceptibility to adaptive attacks, adverse impact on benign samples, high time consumption for conducting a complete defense cycle, etc. To bridge the gap, developing more advanced plug-and-play countermeasures is urgently needed to safeguard these applications. Specifically, this paper first proposes a novel method named Gaussian-augmented GAN-based Adversarial Purification (GA-GAP). Unlike previous methods, GA-GAP enhances the density of the training data in low-robustness regions by using random Gaussian noise. Moreover, GA-GAP incorporates a pre-trained deep learning classifier into the training architecture and integrates its classification loss into the training loss function. Then, following the development of GA-GAP, this paper innovatively proposes a dual-function defense framework named Adversarial Detection on Purification (ADoP) to mitigate adversarial examples further. In ADoP, purification and detection complement each other, achieving the effect of$\mathbf {1+1\gt 2}$, which can more efficiently avoid adaptive attacks. Extensive experiments on ImageNet demonstrate that ADoP outperforms other countermeasures in multiple aspects. These aspects include superior generalization capability in purifying and detecting various adversarial examples, less adverse impact on benign samples, and practical time consumption for conducting a complete defense cycle.

Digital image forensics is a key branch of digital forensics that based on forensic analysis of image authenticity and image content. The advances in new techniques, such as smart devices, Internet of Things (IoT), artificial images, and social networks, make forensic image analysis play an increasing role in a wide range of criminal case investigation. This work focuses on image source identification by analysing both the fingerprints of digital devices and images in IoT environment. A new convolutional neural network (CNN) method is proposed to identify the source devices that token an image in social IoT environment. The experimental results show that the proposed method can effectively identify the source devices with high accuracy.