Adversarial attacks have become a critical focus in visual object tracking (VOT) research. Small, carefully crafted adversarial perturbations to video frames can easily disrupt the visual object tracker, leading to tracking failure. Therefore, studying adversarial attacks contributes to the development of more robust and reliable trackers. Considering that trackers are agnostic in real-world scenarios, research on decision-based black-box attacks is straightforward and practical. However, existing decision-based black-box attacks neither comprehensively analyze the unique characteristics of object tracking nor sufficiently consider the imperceptibility of adversarial perturbations. In this paper, we propose invisible local attack (ILA), a novel decision-based adversarial attack specifically for VOT with imperceptible perturbations. We assume that a significant number of pixels in a frame, irrelevant to the tracked object, do not substantially contribute to the functioning mechanism of a deep tracker. Based on this consideration, we propose a search algorithm to identify the pixel set focused on by the tracker during object tracking. The adversarial noise is then confined to these pixels and iteratively optimized through a heuristic algorithm of ILA. By perturbing only the key pixels, ILA significantly enhances both the attack performance and imperceptibility when it is applied to visual object trackers. Extensive experiments demonstrate that our ILA method achieves a 121% increase in the robustness metric and a 137% improvement in the structural similarity index measure (SSIM) across multiple datasets for various trackers compared with the state-of-the-art (SOTA) method.

Voice authentication has been increasingly adopted for sensitive operations on mobile devices. While voice biometrics can distinguish individuals by their spectral features (such as voiceprints), they are known to be prone to spoofing attacks, where malicious attackers can use pre-recorded or synthesized samples from legitimate users or impersonate the speaking style of the targeted user to deceive the voice authentication system. In this paper, we design and implement a novel software-only anti-spoofing system on smartphones. Our system leverages thepop noise, which is generated by the user’s oral airflow when speaking the passphrase close to the microphone. The pop noise is delicate and subject to user diversity, making it hard to be recorded by replay attacks beyond a certain distance or to be imitated precisely by impersonators. Specifically, we design a new pop noise detection scheme to pinpoint pop noises at the phonemic level, based on which we establish a theoretical model to calculate the sound pressure level from the speech signal in order to get the estimated pressure signal, and then analyze the consistency with the actual pressure signal extracted from the pop noise. Furthermore, we calculate the similarity score of the unique sequences which describe the individually unique relationship between pop noises and phonemes to resist spoofing attacks. Our evaluation on a dataset of 30 participants and three smartphones shows that our system achieves over 94.79% accuracy. Our system requires no additional hardware and is robust to various factors including authentication angle, authentication distance, the length of passphrase, ambient noise, etc.