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Generative drug design facilitates the creation of compounds effective against pathogenic target proteins. This opens up the potential to discover novel compounds within the vast chemical space and fosters the development of innovative therapeutic strategies. However, the practicality of generated molecules is often limited, as many designs focus on a narrow set of drug-related properties, failing to improve the success rate of subsequent drug discovery process. To overcome these challenges, we develop TamGen, a method that employs a GPT-like chemical language model and enables target-aware molecule generation and compound refinement. We demonstrate that the compounds generated by TamGen have improved molecular quality and viability. Additionally, we have integrated TamGen into a drug discovery pipeline and identified 14 compounds showing compelling inhibitory activity against the Tuberculosis ClpP protease, with the most effective compound exhibiting a half maximal inhibitory concentration (IC 50 ) of 1.9 μM. Our findings underscore the practical potential and real-world applicability of generative drug design approaches, paving the way for future advancements in the field. Generative AI holds promise for creating novel compounds. Here, authors introduce TamGen, a GPT-like model designed to generate molecules tailored to specific target proteins. TamGen identified 14 potent compounds against the Tuberculosis ClpP protease, showing its potential for drug discovery.

Robust 3D mesh watermarking is a traditional research topic in computer graphics, which provides an efficient solution to the copyright protection for 3D meshes. Traditionally, researchers need manually design watermarking algorithms to achieve sufficient robustness for the actual application scenarios. In this paper, we propose the first deep learning-based 3D mesh watermarking network, which can provide a more general framework for this problem. In detail, we propose an end-to-end network, consisting of a watermark embedding sub-network, a watermark extracting sub-network and attack layers. We employ the topology-agnostic graph convolutional network (GCN) as the basic convolution operation, therefore our network is not limited by registered meshes (which share a fixed topology). For the specific application scenario, we can integrate the corresponding attack layers to guarantee adaptive robustness against possible attacks. To ensure the visual quality of watermarked 3D meshes, we design the curvature consistency loss function to constrain the local geometry smoothness of watermarked meshes. Experimental results show that the proposed method can achieve more universal robustness while guaranteeing comparable visual quality.

Automatic speaker recognition is an important biometric authentication approach with emerging applications. However, recent research has shown its vulnerability on adversarial attacks. In this paper, we propose a new type of adversarial examples by generating imperceptible adversarial samples for targeted attacks on black-box systems of automatic speaker recognition. Waveform samples are created directly by solving an optimization problem with waveform inputs and outputs, which is more realistic in real-life scenario. Inspired by auditory masking , a regularization term adapting to the energy of speech waveform is proposed for generating imperceptible adversarial perturbations. The optimization problems are subsequently solved by differential evolution algorithm in a black-box manner which does not require any knowledge on the inner configuration of the recognition systems. Experiments conducted on commonly used data sets, LibriSpeech and VoxCeleb, show that the proposed methods have successfully performed targeted attacks on state-of-the-art speaker recognition systems while being imperceptible to human listeners. Given the high SNR and PESQ scores of the yielded adversarial samples, the proposed methods deteriorate less on the quality of the original signals than several recently proposed methods, which justifies the imperceptibility of adversarial samples.

Efforts have been made to safeguard DNNs from intellectual property infringement. Among different techniques, model fingerprinting has gained popularity due to its ability to examine potential infringement without altering the model’s parameters. However, there is a concern regarding the vulnerability of previous model fingerprints to “ambiguity attacks,” where attackers may use fabricated fingerprints to bypass ownership verification, potentially leading to disputes. To address this issue, we propose a dual-verification-based fingerprint authentication system that incorporates the verification of fingerprint genuineness. Briefly, this system involves two authentication processes: conventional fingerprint methods for authenticating model copyrights and the incorporation of copyright information into the fingerprint feature map to confirm ownership of the model fingerprint. Extensive experiments have been conducted to demonstrate the effectiveness of our approach in resisting ambiguity attacks and managing attempts to remove the fingerprint.

Quantum networks can establish End-to-End (E2E) entanglement connections between two arbitrary nodes with desired entanglement fidelity by performing entanglement purification to support quantum applications reliably. The existing works mainly focus on link-level purification scheduling and lack consideration of purifications at network-level, which fails to offer an effective solution for concurrent requests, resulting in low throughput. However, efficiently allocating scarce resources to purify entanglement for concurrent requests remains a critical but unsolved problem. To address this problem, we explore the purification resource scheduling problem from a network-level perspective. We analyze the cost of purification, design the E2E fidelity calculation method in detail, and propose an approach called Purification Scheduling Control (PSC). The basic idea of PSC is to determine the appropriate purification through jointly optimizing purification and resource allocation processes based on conflict avoidance. We conduct extensive experiments that show that PSC can maximize throughput under the fidelity requirement. In quantum networks, entanglement purification is required to ensure that the E2E fidelity of the entanglement connections can support quantum applications reliably. Here, the authors explore the purification resource scheduling problem from a network-level perspective by jointly optimizing purification and resource allocation processes to maximize the throughput under the fidelity requirement.

Combining the color difference formula of CIEDE2000 and the printing industry standard for visual verification, we present an objective color image quality assessment method correlated with subjective vision perception. An objective score conformed to subjective perception (OSCSP) Q was proposed to directly reflect the subjective visual perception. In addition, we present a general method to calibrate correction factors of color difference formula under real experimental conditions. Our experiment results show that the present DE2000-based metric can be consistent with human visual system in general application environment.

Face swapping is an intriguing and intricate task in the field of computer vision. Currently, most mainstream face swapping methods employ face recognition models to extract identity features and inject them into the generation process. Nonetheless, such methods often struggle to effectively transfer identity information, which leads to generated results failing to achieve a high identity similarity to the source face. Furthermore, if we can accurately disentangle identity information, we can achieve controllable face swapping, thereby providing more choices to users. In pursuit of this goal, we propose a new face swapping framework (ControlFace) based on the disentanglement of identity information. We disentangle the structure and texture of the source face, encoding and characterizing them in the form of feature embeddings separately. According to the semantic level of each feature representation, we inject them into the corresponding feature mapper and fuse them adequately in the latent space of StyleGAN. Owing to such disentanglement of structure and texture, we are able to controllably transfer parts of the identity features. Extensive experiments and comparisons with state-of-the-art face swapping methods demonstrate the superiority of our face swapping framework in terms of transferring identity information, producing high-quality face images, and controllable face swapping.

Deepfake aims to swap a face of an image with someone else’s likeness in a reasonable manner. Existing methods usually perform deepfake frame by frame, thus ignoring video consistency and producing incoherent results. To address such a problem, we propose a novel framework Neural Identity Carrier (NICe), which learns identity transformation from an arbitrary face-swapping proxy via a U-Net. By modeling the incoherence between frames as noise, NICe naturally suppresses its disturbance and preserves primary identity information. Concretely, NICe inputs the original frame and learns transformation supervised by swapped pseudo labels. As the temporal incoherence has an uncertain or stochastic pattern, NICe can filter out such outliers and well maintain the target content by uncertainty prediction. With the predicted temporally stable appearance, NICe enhances its details by constraining 3D geometry consistency, making NICe learn fine-grained facial structure across the poses. In this way, NICe guarantees the temporal stableness of deepfake approaches and predicts detailed results against over-smoothness. Extensive experiments on benchmarks demonstrate that NICe significantly improves the quality of existing deepfake methods on video-level. Besides, data generated by our methods can benefit video-level deepfake detection methods.

Reversible data hiding has extensive applications in fields like data authentication, medical data management and error concealment. In this paper, we formulate the model of reversible data hiding over a special ternary cover that is suitable for any transform domain, such as DCT domain, where the probability density function of the transformed coefficients has a Laplacian distribution with a small variance. After deriving rate-distortion function for this model, we propose a code construction that can approach the rate-distortion bound. Based on the code construction, a reversible data hiding method for JPEG images is proposed. Experimental results demonstrate that proposed method has a good balance among image quality, filesize increment and computation time. The excellent performance of proposed method also demonstrate the power of our code construction for reversible data hiding on DCT based media.

In the traditional secret image sharing (SIS) scheme, the secret image is divided into several noise-like shares, which lack authentication and may attract the attention of malicious users. Therefore, the authenticability of shadow images may play an important role and is worthy of investigation. Traditional shadow authentication research requires additional image or additional bits for authentication, which may lead to high complexity. In this paper, we propose a novel ( k ,  n ) threshold SIS scheme that is based on the Chinese remainder theorem (CRT) with shadow authenticability. Our contribution is that the secret grayscale image is distributed into n shadows, while each shadow image contains authentication information with QR code embedding. Our scheme can realize the 100% detection rate of fake participants when a credible control center is involved. The experimental results confirm that the proposed scheme has low shadow generation, authentication complexity, and the lossless recovery of secret image.