Vision Transformers (ViTs) have emerged as a compelling alternative to Convolutional Neural Networks (CNNs) in the realm of computer vision, showcasing tremendous potential. However, recent research has un-veiled a susceptibility of ViTs to adversarial attacks, akin to their CNN counterparts. Adversarial training and randomization are two representative effective defenses for CNNs. Some researchers have attempted to apply adversarial training to ViTs and achieved comparable robustness to CNNs, while it is not easy to directly apply randomization to ViTs because of the architecture difference between CNNs and ViTs. In this paper, we delve into the structural intricacies of ViTs and propose a novel defense mechanism termed Random entangled image Transformer (ReiT), which seamlessly integrates adversarial training and randomization to bolster the adversarial robustness of ViTs. Recognizing the challenge posed by the structural disparities between ViTs and CNNs, we introduce a novel module, input-independent random entangled self-attention (II-ReSA). This module op-timizes random entangled tokens that lead to “dissimilar” self-attention outputs by leveraging model parameters and the sampled random tokens, thereby synthesizing the self-attention module outputs and random entangled tokens to diminish adversarial similarity. ReiT incorporates two distinct random entangled tokens and employs dual randomization, offering an effective countermeasure against adversarial examples while ensuring comprehensive deduction guarantees. Through extensive experiments conducted on various ViT variants and benchmarks, we substantiate the superiority of our proposed method in enhancing the adversarial robustness of Vision Transformers.

Authentication and cryptography are critical security functions and, thus, are very often included as part of code. These functions require using credentials, such as passwords, security tokens, and cryptographic keys. However, developers often incorrectly implement/use credentials in their code because of a lack of secure coding skills. This paper analyzes open-source projects concerning the correct use of security credentials. We developed a semantic-rich, language-independent analysis approach for analyzing many projects automatically. We implemented a detection tool, SEAGULL, to automatically check open-source projects based on string literal and code structure information. Instead of analyzing the entire project code, which might result in path explosion when constructing data and control dependencies, SEAGULL pinpoints all literal constants to identify credential candidates and then analyzes the code snippets correlated to these candidates. SEAGULL accurately identifies the leaked credentials by obtaining semantic and syntax information about the code. We applied SEAGULL to 377 open-source projects. SEAGULL successfully reported 19 real-world credential leakages out of those projects. Our analysis shows that some developers protected or erased the credentials in the current project versions, but previously used credentials can still be extracted from the project’s historical versions. Although the implementations of credential leakages seem to be fixed in the current projects, attackers could successfully log into accounts if developers keep using the same credentials as before. Additionally, we found that such credential leakages still affect some projects. By exploiting leaked credentials, attackers can log into particular accounts.

Deep neural networks (DNNs) have achieved state-of-the-art performance on face recognition (FR) tasks in the last decade. In real scenarios, the deployment of DNNs requires taking various face accessories into consideration, like glasses, hats, and masks. In the COVID-19 pandemic era, wearing face masks is one of the most effective ways to defend against the novel coronavirus. However, DNNs are known to be vulnerable to adversarial examples with a small but elaborated perturbation. Thus, a facial mask with adversarial perturbations may pose a great threat to the widely used deep learning-based FR models. In this paper, we consider a challenging adversarial setting: targeted attack against FR models. We propose a new stealthy physical masked FR attack via adversarial style optimization. Specifically, we train an adversarial style mask generator that hides adversarial perturbations inside style masks. Moreover, to ameliorate the phenomenon of sub-optimization with one fixed style, we propose to discover the optimal style given a target through style optimization in a continuous relaxation manner. We simultaneously optimize the generator and the style selection for generating strong and stealthy adversarial style masks. We evaluated the effectiveness and transferability of our proposed method via extensive white-box and black-box digital experiments. Furthermore, we also conducted physical attack experiments against local FR models and online platforms.

In this brief, future equality-constrained quadratic programming (FECQP) is studied. Via a zeroing neurodynamics method, a continuous-time zeroing neurodynamics (CTZN) model is presented. By using Taylor-Zhang discretization formula to discretize the CTZN model, a Taylor-Zhang discrete-time zeroing neurodynamics (TZ-DTZN) model is presented to perform FECQP. Furthermore, we focus on the critical parameter of the TZ-DTZN model, i.e., stepsize. By theoretical analyses, we obtain an effective range of the stepsize, which guarantees the stability of the TZ-DTZN model. In addition, we further discuss the optimal value of the stepsize, which makes the TZ-DTZN model possess the optimal stability (i.e., the best stability with the fastest convergence). Finally, numerical experiments and application experiments for motion generation of a robot manipulator are conducted to verify the high precision of the TZ-DTZN model and the effective range and optimal value of the stepsize for FECQP.

Discrete time-varying problems are frequently encountered in mathematics and engineering fields, such as numerical analysis, signal processing and computer computing. However, conventional algorithms mainly solve time-invariant problems. Employed for discrete time-varying problems solving, conventional algorithms may generate quite large and unacceptable lagging errors. In this paper, discrete time-varying four fundamental operations (DTVFFOs) are studied. In order to eliminate the lagging errors, based on the zeroing dynamics (ZD) and Taylor-Zhang discretization formula, discrete computing models, which are termed T-Z-K and T-Z-U models, are proposed and investigated. Note that the aforementioned models have an error pattern of O(g3), where g denotes the sampling gap. For comparison, Euler-type discrete models and Newton iteration (NI) models are also presented. Eventually, illustrative numerical experiments are displayed to testify the great performances of the proposed Taylor-Zhang discrete ZD models.