Substitute training for black-box attacks, leveraging query synthesis, has recently gained significant attention, especially in data-free scenarios. Existing works focus on extracting the victim model without exploring the deployment of adversarial attacks, leading to inefficiencies and redundant queries. To address these issues, we present a novel data-free substitute training attack framework that focuses on aligning the ’chain of attack’ rather than the decision boundary of models. Specifically, we propose a Temporal Contrastive Sampling module to capture essential discriminative features. A Simplified Adversarial Training module is further introduced to restrict the routes crossing the decision boundary. For catastrophic forgetting and data imbalance considerations, we integrate a Substitute Model Resetting module strategy. Comprehensive experiments over three datasets demonstrate our effectiveness: under decision-based settings, we can achieve targeted attack success rates of 81.87% and 91.89% on the CIFAR-10 dataset with query budgets of 500k and 1M, surpassing state-of-the-art methods under score-based settings.

In autonomous assistance systems, accurate camera vision is indispensable for driving safety. Featuring this safety-critical scenario, physical adversarial examples are arguably the most threatening. However, existing physical adversarial attacks are either conspicuous or ineffective, leaving a dilemma for balancing between attack effectiveness and stealthiness. In this paper, we put forth a new type of adversarial patch attack, leveraging the behavior characteristic of high-speed shutters in autonomous driving cameras. Instead of deploying static images onto real-world objects, we embed adversarial examples into videos and cast them with projectors. By delicately deciding the frame contents and display frequencies, the adversarial frame contents are almost invisible to humans, but high-speed shutters can capture them (see our demos (https://anonymous.4open.science/r/7003)). We demonstrate the attack feasibility by fooling traffic sign detectors, altering speed limit signs to be mis-detected or undetected, and hence manipulating the driving speed of victims. We conduct extensive experiments under various conditions, confirming that our new attack is effective and robust: It can deceive state-of-the-art detector models with success rates of 99% and over 90% in untargeted and targeted manners, respectively. Our further investigations unravel the transferability of our attack to other detectors in a black-box setting.

Face authentication usually utilizes deep learning models to verify users with high accuracy. However, it is vulnerable to various attacks that cheat the models by manipulating the digital counterparts of human faces. So far, lots of liveness detection schemes have been developed to prevent such attacks. Unfortunately, the attacker can still bypass them by constructing sophisticated attacks. We study the security of existing face authentication services and typical liveness detection approaches. Particularly, we develop a new type of attack, i.e., the low-cost 3D projection attack that projects manipulated face videos on a 3D face model, which can easily evade these face authentication services and liveness detection approaches. To this end, we propose FaceLip, a novel face liveness detection scheme on mobile devices, which utilizes lip motion patterns built upon well-designed acoustic signals to enable a strong security guarantee. The unique lip motions for each user are unforgeable because FaceLip verifies the patterns by analyzing acoustic signals that are dynamically generated according to random challenges, which ensures that our signals for liveness detection cannot be manipulated. We prototype FaceLip on off-the-shelf smartphones and conduct extensive experiments under different settings. Our evaluation with 44 participants validates the effectiveness and robustness of FaceLip.

MOTIVATION: Longitudinal studies increasingly collect rich 'omics' data sampled frequently over time and across large cohorts to capture dynamic health fluctuations and disease transitions. However, the generation of longitudinal omics data has preceded the development of analysis tools that can efficiently extract insights from such data. In particular, there is a need for statistical frameworks that can identify not only which omics features are differentially regulated between groups but also over what time intervals. Additionally, longitudinal omics data may have inconsistencies, including non-uniform sampling intervals, missing data points, subject dropout and differing numbers of samples per subject. RESULTS: In this work, we developed OmicsLonDA, a statistical method that provides robust identification of time intervals of temporal omics biomarkers. OmicsLonDA is based on a semi-parametric approach, in which we use smoothing splines to model longitudinal data and infer significant time intervals of omics features based on an empirical distribution constructed through a permutation procedure. We benchmarked OmicsLonDA on five simulated datasets with diverse temporal patterns, and the method showed specificity greater than 0.99 and sensitivity greater than 0.87. Applying OmicsLonDA to the iPOP cohort revealed temporal patterns of genes, proteins, metabolites and microbes that are differentially regulated in male versus female subjects following a respiratory infection. In addition, we applied OmicsLonDA to a longitudinal multi-omics dataset of pregnant women with and without preeclampsia, and OmicsLonDA identified potential lipid markers that are temporally significantly different between the two groups. AVAILABILITY AND IMPLEMENTATION: We provide an open-source R package (https://bioconductor.org/packages/OmicsLonDA), to enable widespread use. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

With the increasing popularity of Android platform, Android malware, especially APP-Repackaging malware wherein the malicious code is injected into legitimate Android applications, is spreading rapidly. This paper proposes a new system named MIGDroid, which leverages method invocation graph based static analysis to detect APP-Repackaging Android malware. The method invocation graph reflects the “interaction” connections between different methods. Such graph can be naturally exploited to detect APP-Repackaging malware because the connections between injected malicious code and legitimate applications are expected to be weak. Specifically, MIGDroid first constructs method invocation graph on the smali code level, and then divides the method invocation graph into weakly connected sub-graphs. To determine which sub-graph corresponds to the injected malicious code, the threat score is calculated for each sub-graph based on the invoked sensitive APIs, and the subgraphs with higher scores will be more likely to be malicious. Experiment results based on 1,260 Android malware samples in the real world demonstrate the specialty of our system in detecting APP-Repackaging Android malware, thereby well complementing existing static analysis systems (e.g., Androguard) that do not focus on APP-Repackaging Android malware.

The design technique of signal processing of sky wave OTH-B radar is presented as well as 3D treatments in space, time and frequency domains and their performances. The technical insurance of a bistatic synchronization system and the characteristic of signal processing when detecting ship targets are reported briefly. The performances of a subarray are tested through several groups of experimental data from an outside experiment.>