Renjie Lu

Renjie Lu, Jingke Meng, Wei-Shi Zheng

Vision and language navigation is a task that requires an agent to navigate according to a natural language instruction. Recent methods predict sub-goals on constructed topology map at each step to enable long-term action planning. However, they suffer from high computational cost when attempting to support such high-level predictions with GCN-like models. In this work, we propose an alternative method that facilitates navigation planning by considering the alignment between instructions and directed fidelity trajectories, which refers to a path from the initial node to the candidate locations on a directed graph without detours. This planning strategy leads to an efficient model while achieving strong performance. Specifically, we introduce a directed graph to illustrate the explored area of the environment, emphasizing directionality. Then, we firstly define the trajectory representation as a sequence of directed edge features, which are extracted from the panorama based on the corresponding orientation. Ultimately, we assess and compare the alignment between instruction and different trajectories during navigation to determine the next navigation target. Our method outperforms previous SOTA method BEVBert on RxR dataset and is comparable on R2R dataset while largely reducing the computational cost. Code is available: https://github.com/iSEE-Laboratory/VLN-PRET.

Jun Yu, Shenshen Du, Guochen Xie et al.

Synthetic Aperture Radar (SAR) to electro-optical (EO) image translation is a fundamental task in remote sensing that can enrich the dataset by fusing information from different sources. Recently, many methods have been proposed to tackle this task, but they are still difficult to complete the conversion from low-resolution images to high-resolution images. Thus, we propose a framework, SAR2EO, aiming at addressing this challenge. Firstly, to generate high-quality EO images, we adopt the coarse-to-fine generator, multi-scale discriminators, and improved adversarial loss in the pix2pixHD model to increase the synthesis quality. Secondly, we introduce a denoising module to remove the noise in SAR images, which helps to suppress the noise while preserving the structural information of the images. To validate the effectiveness of the proposed framework, we conduct experiments on the dataset of the Multi-modal Aerial View Imagery Challenge (MAVIC), which consists of large-scale SAR and EO image pairs. The experimental results demonstrate the superiority of our proposed framework, and we win the first place in the MAVIC held in CVPR PBVS 2023.

Zhenjie Liu, Jianzhang Lu, Renjie Lu et al.

Recent advancements in video diffusion models have significantly enhanced audio-driven portrait animation. However, current methods still suffer from flickering, identity drift, and poor audio-visual synchronization. These issues primarily stem from entangled appearance-motion representations and unstable inference strategies. In this paper, we introduce \textbf{ConsistTalk}, a novel intensity-controllable and temporally consistent talking head generation framework with diffusion noise search inference. First, we propose \textbf{an optical flow-guided temporal module (OFT)} that decouples motion features from static appearance by leveraging facial optical flow, thereby reducing visual flicker and improving temporal consistency. Second, we present an \textbf{Audio-to-Intensity (A2I) model} obtained through multimodal teacher-student knowledge distillation. By transforming audio and facial velocity features into a frame-wise intensity sequence, the A2I model enables joint modeling of audio and visual motion, resulting in more natural dynamics. This further enables fine-grained, frame-wise control of motion dynamics while maintaining tight audio-visual synchronization. Third, we introduce a \textbf{diffusion noise initialization strategy (IC-Init)}. By enforcing explicit constraints on background coherence and motion continuity during inference-time noise search, we achieve better identity preservation and refine motion dynamics compared to the current autoregressive strategy. Extensive experiments demonstrate that ConsistTalk significantly outperforms prior methods in reducing flicker, preserving identity, and delivering temporally stable, high-fidelity talking head videos.

Renjie Lu, Haihua Shen, Feng Zhang et al.

Hardware Trojans (HTs) have drawn more and more attention in both academia and industry because of its significant potential threat. In this paper, we proposed a novel HT detection method using information entropy based clustering, named HTDet. The key insight of HTDet is that the Trojan usually be inserted in the regions with low controllability and low observability in order to maintain high concealment, which will result in that Trojan logics appear extremely low transitions during the simulation. This means that the logical regions with the low transitions will provide us with much more abundant and more important information for HT detection. Therefore, HTDet applies information theory technology and a typical density-based clustering algorithm called Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to detect all suspicious Trojan logics in circuit under detection (CUD). DBSCAN is an unsupervised learning algorithm, which can improve the applicability of HTDet. Besides, we develop a heuristic test patterns generation method using mutual information to increase the transitions of suspicious Trojan logics. Experimental evaluation with benchmarks demenstrates the effectiveness of HTDet.

Renjie Lu

Nowadays, with the booming development of Internet and software industry, more and more malware variants are designed to perform various malicious activities. Traditional signature-based detection methods can not detect variants of malware. In addition, most behavior-based methods require a secure and isolated environment to perform malware detection, which is vulnerable to be contaminated. In this paper, similar to natural language processing, we propose a novel and efficient approach to perform static malware analysis, which can automatically learn the opcode sequence patterns of malware. We propose modeling malware as a language and assess the feasibility of this approach. First, We use the disassembly tool IDA Pro to obtain opcode sequence of malware. Then the word embedding technique is used to learn the feature vector representation of opcode. Finally, we propose a two-stage LSTM model for malware detection, which use two LSTM layers and one mean-pooling layer to obtain the feature representations of opcode sequences of malwares. We perform experiments on the dataset that includes 969 malware and 123 benign files. In terms of malware detection and malware classification, the evaluation results show our proposed method can achieve average AUC of 0.99 and average AUC of 0.987 in best case, respectively.

Renjie Lu

Recently, with the booming development of software industry, more and more malware variants are designed to perform malicious behaviors. The evolution of malware makes it difficult to detect using traditional signature-based methods. Moreover, malware detection has important effect on system security. In this paper, we present SCGDet, which is a novel malware detection method based on system call graph model (SCGM). We first develop a system call pruning method, which can exclude system calls that have little impact on malware detection. Then we propose the SCGM, which can capture the semantic features of run-time program by grouping the system calls based on the reachability relation. We aim to obtain the generic representation of malicious behaviors with similar system call patterns. We evaluate the performance of SCGDet using different machine learning algorithms on the dataset including 854 malware samples and 740 benign samples. Compared with the traditional n-gram method, the SCGDet has the smaller feature space, the higher detection accuracy and the lower false positives. Experimental results show that SCGDet can reduce the average FPR of 14.75% and improve the average Accuracy of 8.887%, and can obtain a TPR of 97.44%, an FPR of 1.96% and an Accuracy of 97.78% in the best case.