We propose Contextual History for Adaptive and Simple Exploitation (CHASE), a novel multi-turn method for Large Language Model (LLM) jailbreaking. Rather than directly attack an LLM that may be difficult to jailbreak, CHASE first collects jailbroken histories from an easy-to-jailbreak LLM and then transfers them to the target LLM. Through this history transfer process, CHASE misleads the target LLM into thinking that it is responsible for producing the jailbroken histories and increases the chances of successful jailbreaking by prompting it to continue the conversation. Extensive evaluations on mainstream LLMs show that CHASE consistently achieves higher attack success rates and demands fewer computational resources compared to existing methods.

Contemporary deep learning approaches for optical flow estimation continue to face persistent challenges in model interpretability, generalization capacity, and deployment efficiency, significantly constraining their practical implementation. This limitation becomes particularly critical in applications such as visual odometry (VO), where precise sparse point tracking supersedes the conventional emphasis on dense optical flow accuracy. Moreover, the lack of a joint framework combining keypoint detection and optical flow estimation limits sparse optical flow performance. To address these fundamental issues, we propose a novel dual-task imperative learning framework that synergistically optimizes sparse optical flow estimation (iFLOW) with adaptive keypoint detection (iPOINT). Our methodology implements an Expectation-Maximization (EM) paradigm where iFLOW and iPOINT undergo alternating optimization through a Gauss-Newton reasoning engine. This innovative architecture leverages convolutional feature advantages under the generalized feature invariance principle. The resulting imperative learning mechanism imbues our framework with enchanced interpretability and cross-domain adaptability while maintaining computational efficiency. Through comparative evaluations against classical and learning-based baselines, our ultra-compact models (0.05M parameters for iFLOW, 0.09M for iPOINT) demonstrate remarkable performance across multiple metrics (End-point Error, F1-all, VO trajectory accuracy) despite requiring only 200 training image pairs.

To tackle with advanced persistent threats against industrial control system, Siemens has developed S7CommPlus- TLS, a new version of the encrypted protocol challenging traditional DPI-based anomaly detection methods. However, the communication mode of industrial control system leads to the overlapping of periodic traffic and sensitive behavior traffic, and thus makes mainstream encrypted traffic classification methods exhibit a poor performance in S7CommPlus-TLS protocol. Therefore, we design a multiple clustering framework called MCFM, which can automatically extract sensitive behavior of S7CommPlus-TLS from network traffic. The first-clustering is used as a pre-processing model to separate and remove periodic traffic from overlapping flows according to the communication mode of industrial control system. Besides, we employ the second- clustering as a generator to extract the fingerprint of sensitive behaviors. Our comprehensive experiments on the simulation dataset covering six sensitive behaviors indicate that MCFM achieves an excellent performance, and outperforms present cutting-edge methods. To the best of our knowledge, this is the first work analyzing industrial control system from the perspective of encrypted traffic analysis.

Abstract Product surface quality inspection based on machine vision has been paid more and more attention in modern industrial production. Multidimensional feature information fusion also plays an important role in the detection rate and accuracy of steel plate defects. Based on the machine vision based defect detection of steel plate surface, this article mainly studies the collection, extraction and fusion of multidimensional feature information. The visual imaging system, the surface defect detection technology based on vision, the depth camera and the light source type are briefly reviewed. At the same time, the roadmap and related methods of multidimensional feature information acquisition system are studied. Finally, the multidimensional fusion defect detection technology is prospected and summarized.

Abstract In the production and construction of industry, safety accidents caused by unsafe behaviors of staff often occur. In a complex construction site scene, due to improper operations by personnel, huge safety risks will be buried in the entire production process. The use of deep learning algorithms to replace manual monitoring of site safety regulations is a powerful guarantee for sticking to the line of safety in production. First, the improved YOLO v3 algorithm is used to output the predicted anchor box of the target object, and then pixel feature statistics are performed on the anchor box, and the weight coefficients are respectively multiplied to output the confidence of the standard wearing of the helmet in each predicted anchor box area, according to the empirical threshold determine whether workers meet the standards for wearing helmets. Experimental results show that the helmet wearing detection algorithm based on deep learning in this paper increases the feature map scale, optimizes the prior dimensional algorithm of specific helmet dataset, and improves the loss function, and then combines image processing pixel feature statistics to accurately detect whether the helmet is worn by the standard. The final result is that mAP reaches 93.1% and FPS reaches 55 f/s. In the helmet recognition task, compared to the original YOLO v3 algorithm, mAP is increased by 3.5% and FPS is increased by 3 f/s. It shows that the improved detection algorithm has a better effect on the detection speed and accuracy of the helmet detection task.

Summary Recently, most deep convolutional neural networks used for image super‐resolution have achieved impressive performance on ideal datasets. However, these methods always fail in real‐world super‐resolution, and the results are blurred and structurally deformed. In this paper, a multiscale generative adversarial network (MGAN) is proposed to alleviate these issues. The model's multiscale loss function can effectively reduce the solution space and obtain the best features to reconstruct the image. The degraded framework based on kernel estimation and noise injection is mainly applied to obtain LR images that share the same domain with real‐world pictures. Moreover, the gradient branch is presented to provide other structural priors for SR processing. Simultaneously, to obtain better visual effects, LPIPS is used for perceptual losses instead of Visual Geometry Group (VGG). The competitive results show that our MGAN model outperforms the state‐of‐the‐art methods, resulting in lower noise and better visual quality, and reflects the superiority in image structure restoration.

Person reidentification (re‐id) suffers from a challenging issue due to the significant inconsistency of the camera network, including position, view, and brands. In this paper, we propose a deep camera‐aware metric learning (DCAML) model, where images on the identity‐level spaces are further projected into different camera‐level subspaces, which can explore the inherent relationship between identity and camera. Furthermore, we exploit dynamic training strategy to jointly multiple metrics for identity‐camera relationship learning and thus consumedly elevating the retrieval accuracy. Extensive experiments on the three public datasets demonstrated that our method performs competitive results compared to the state‐of‐the‐art person re‐id methods.