Abstract Static analysis plays a crucial role in program optimization, bug detection, and automated testing. Dyck-reachability provides a foundational formulation for static analysis, as Dyck grammars can model critical properties such as field and context sensitivity, thus offering broad applicability. This paper shows that static analysis problems modeled as Dyck-reachability on bidirected graphs can be encoded into the EUF SMT theory; consequently, all such problems admit efficient formulation and solution via EUF-based SMT solvers. By leveraging the optimized nature of modern SMT solvers, our method achieves efficiency comparable to state-of-the-art graph-based bidirected Dyck-reachability algorithms while eliminating the need for developing complex specialized graph reachability algorithms. Our approach opens new avenues for solving these classical static analysis problems, demonstrating the strong potential of SMT solvers in encoding static analysis solutions.

Recurrent Neural Networks (RNNs) are widely used in control system due to their dynamic capabilities. However, the control accuracy of RNN-based systems can be compromised by noise interference, and there has been little research on RNN-based control in disturbed multi-agent systems. To address this, we developed an enhanced Distributed RNN (DRNN) structure and proposed a Novel DRNN-based Control Protocol (NDRNN-CP). This enhancement involves introducing a time-delay component, allowing the protocol to adaptively learn noise variation patterns. As a result, the NDRNN-CP effectively resists various periodic noise interferences and achieves more precise control of each agent. Additionally, our optimized activation function ensures that all agents reach consensus within a predefined time. To demonstrate the advantages of NDRNN-CP, we conducted extensive experiments that confirmed its significant improvements in noise signal resistance and convergence performance.

Abstract serves as an automatic test generation tool designed for C programs based on symbolic execution. employs fuzzing-based pre-analysis and combines static symbolic execution and dynamic symbolic execution to improve the effectiveness of test generation. achieves 5132 scores and is ranked 4th in the branch coverage track of Test-Comp 2024.

This paper presents the summary report on the DeepFake Game Competition on Visual Realism Assessment (DFGC-VRA). Deep-learning based face-swap videos, also known as deepfakes, are becoming more and more realistic and deceiving. The malicious usage of these face-swap videos has caused wide concerns. There is a ongoing deepfake game between its creators and detectors, with the human in the loop. The research community has been focusing on the automatic detection of these fake videos, but the assessment of their visual realism, as perceived by human eyes, is still an unexplored dimension. Visual realism assessment, or VRA, is essential for assessing the potential impact that may be brought by a specific face-swap video, and it is also useful as a quality metric to compare different face-swap methods. This is the third edition of DFGC competitions, which focuses on the new visual realism assessment topic, different from previous ones that compete creators versus detectors. With this competition, we conduct a comprehensive study of the SOTA performance on the new task. We also release our MindSpore codes to further facilitate research in this field (https://github.com/bomb2peng/DFGC-VRA-benckmark).

Face swapping has drawn a lot of attention for its compelling performance. However, current deepfake methods suffer the effects of obscure workflow and poor performance. To solve these problems, we present DeepFaceLab, the current dominant deepfake framework for practical face-swapping. It provides the necessary tools as well as an easy-to-use way to conduct high-quality face-swapping. It also offers a flexible and loose coupling structure for people who need to strengthen their pipeline with other features without writing complicated boilerplate code. We detail the principles that drive the implementation of DeepFaceLab and introduce its pipeline. DeepFaceLab could achieve cinema-level results with high fidelity as our supplemental video shows. We also demonstrate the advantage of our system by comparing our approach with other face-swapping methods. Deepfake defense not only requires the research of detection but also requires the efforts of generation methods. As for a popular and practical toolkit, we encourage users to promote harmless deepfake-entertainment content on social media, reminding the public of the existence of deepfake when they are looking for entertainment.

This paper presents a novel Physically-guided Disentangled Implicit Rendering (PhyDIR) framework for highfidelity 3D face modeling. The motivation comes from two observations: Widely-used graphics renderers yield excessive approximations against photo-realistic imaging, while neural rendering methods produce superior appearances but are highly entangled to perceive 3D-aware operations. Hence, we learn to disentangle the implicit rendering via explicit physical guidance, while guaranteeing the properties of: (1) 3D-aware comprehension and (2) high-reality image formation. For the former one, PhyDIR explicitly adopts 3D shading and rasterizing modules to control the renderer, which disentangles the light, facial shape, and viewpoint from neural reasoning. Specifically, PhyDIR proposes a novel multi-image shading strategy to compensate for the monocular limitation, so that the lighting variations are accessible to the neural renderer. For the latter, PhyDIR learns the face-collection implicit texture to avoid ill-posed intrinsic factorization, then leverages a series of consistency losses to constrain the rendering robustness. With the disentangled method, we make 3D face modeling benefit from both kinds of rendering strategies. Extensive experiments on benchmarks show that PhyDIR obtains superior performance than state-of-the-art explicit/implicit methods on geometry/texture modeling.

As an effective method for intellectual property (IP) protection, model watermarking technology has been applied on a wide variety of deep neural networks (DNN), including speech classification models. However, how to design a black-box watermarking scheme for automatic speech recognition (ASR) models is still an unsolved problem, which is a significant demand for protecting remote ASR Application Programming Interface (API) deployed in cloud servers. Due to conditional independence assumption and label-detection-based evasion attack risk of ASR models, the black-box model watermarking scheme for speech classification models cannot apply to ASR models. In this paper, we propose the first black-box model watermarking framework for protecting the IP of ASR models. Specifically, we synthesize trigger audios by spreading the speech clips of model owners over the entire input audios and labeling the trigger audios with the stego texts, which hides the authorship information with linguistic steganography. Experiments on the state-of-the-art open-source ASR system DeepSpeech demonstrate the feasibility of the proposed watermarking scheme, which is robust against five kinds of attacks and has little impact on accuracy.

Recently almost all the mainstream deepfake detection methods use Convolutional Neural Networks (CNN) as their backbone. However, due to the overreliance on local texture information which is usually determined by forgery methods of training data, these CNN-based methods cannot generalize well to unseen data. To get out of the predicament of prior methods, in this paper, we propose a novel transformer-based framework to model both global and local information and analyze anomalies of face images. In particular, we design attention leading module, multi-forensics module and variant residual connections for deepfake detection, and leverage token-level contrast loss for more detailed supervision. Experiments on almost all popular public deepfake datasets demonstrate that our method achieves state-of-the-art performance in cross-dataset evaluation and comparable performance in intra-dataset evaluation.

Invertible grayscale is a special kind of grayscale from which the original color can be recovered. Given an input color image, this seminal work tries to hide the color information into its grayscale counterpart while making it hard to recognize any anomalies. This powerful functionality is enabled by training a hiding sub-network and restoring sub-network in an end-to-end way. Despite its expressive results, two key limitations exist: 1) The restored color image often suffers from some noticeable visual artifacts in the smooth regions. 2) It is very sensitive to JPEG compression, i.e., the original color information cannot be well recovered once the intermediate grayscale image is compressed by JPEG. To overcome these two limitations, this article introduces adversarial training and JPEG simulator respectively. Specifically, two auxiliary adversarial networks are incorporated to make the intermediate grayscale images and final restored color images indistinguishable from normal grayscale and color images. And the JPEG simulator is utilized to simulate real JPEG compression during the online training so that the hiding and restoring sub-networks can automatically learn to be JPEG robust. Extensive experiments demonstrate that the proposed method is superior to the original invertible grayscale work both qualitatively and quantitatively while ensuring the JPEG robustness. We further show that the proposed framework can be applied under different types of grayscale constraints and achieve excellent results.

Recently, Li et al. proposed an effective non-additive distortion model for JPEG steganography by preserving Block Boundary Continuity (BBC) in the spatial domain. However, the method based on BBC only explored how the modifications of DCT coefficient pairs at the same mode in adjacent blocks will impact on BBC. In this paper, we propose a method to enhance the BBC, called BBC++, by considering the mutual impact on the BBC from all DCT coefficients in adjacent blocks. To do that, we design updating strategies for both covers and costs in multi-round embedding processions. During the embedding, the cover is updated to repair the BBC after embedding and the corresponding costs are updated to keep the BBC from being destroyed in the next embedding. Experimental results show that the BBC++ can better maintain BBC and outperform previous non-additive distortion steganography when resisting modern JPEG steganalyzers.

Deep neural networks have made tremendous progress in 3D point-cloud recognition. Recent works have shown that these 3D recognition networks are also vulnerable to adversarial samples produced from various attack methods, including optimization-based 3D Carlini-Wagner attack, gradient-based iterative fast gradient method, and skeleton-detach based point-dropping. However, after a careful analysis, these methods are either extremely slow because of the optimization/iterative scheme, or not flexible to support targeted attack of a specific category. To overcome these shortcomings, this paper proposes a novel label guided adversarial network (LG-GAN) for real-time flexible targeted point cloud attack. To the best of our knowledge, this is the first generation based 3D point cloud attack method. By feeding the original point clouds and target attack label into LG-GAN, it can learn how to deform the point clouds to mislead the recognition network into the specific label only with a single forward pass. In detail, LG-GAN first leverages one multi-branch adversarial network to extract hierarchical features of the input point clouds, then incorporates the specified label information into multiple intermediate features using the label encoder. Finally, the encoded features will be fed into the coordinate reconstruction decoder to generate the target adversarial sample. By evaluating different point-cloud recognition models (e.g., PointNet, PointNet++ and DGCNN), we demonstrate that the proposed LG-GAN can support flexible targeted attack on the fly while guaranteeing good attack performance and higher efficiency simultaneously.