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A key challenge of image splicing detection is how to localize integral tampered regions without false alarm. Although current forgery detection approaches have achieved promising performance, the integrality and false alarm are overlooked. In this paper, we argue that the insufficient use of splicing boundary is a main reason for poor accuracy. To tackle this problem, we propose an Edge-enhanced Transformer (ET) for tampered region localization. Specifically, to capture rich tampering traces, a two-branch edge-aware transformer is built to integrate the splicing edge clues into the forgery localization network, generating forgery features and edge features.

In this letter, we propose a novel solution to the problem of single image super-resolution at multiple scaling factors, with a single network architecture. In applications where only a detail needs to be super-resolved, traditional solutions must choose to use as input either the low-resolution detail, thus losing the information about the context, or the whole low-resolution image and then crop the desired output detail, which is quite wasteful in terms of computations and storage. 

Active reconfigurable intelligent surfaces (RISs) are a novel and promising technology that allows controlling the radio propagation environment while compensating for the product path loss along the RIS-assisted path. In this letter, we consider the classical radar detection problem and propose to use an active RIS to get a second independent look at a prospective target illuminated by the radar transmitter.

Recent years have witnessed remarkable success of Graph Fourier Transform (GFT) in point cloud attribute compression. Existing researches mainly utilize geometry distance to define graph structure for coding attribute (e.g., color), which may distribute high weights to the edges connecting points across texture boundaries. 

This paper addresses the target localization problem using time-of-arrival (TOA)-based technique under the non-line-of-sight (NLOS) environment. To alleviate the adverse effect of the NLOS error on localization, a total least square framework integrated with a regularization term (RTLS) is utilized, and with which the localization problem can get rid of the ill-posed issue. However, it is challenging to figure out the exact solution for the considered localization problem.

Deep neural networks in deep learning have been widely demonstrated to have higher accuracy and distinct advantages over traditional machine learning methods in extracting data features. While convolutional neural networks (CNNs) have shown great success in feature extraction and audio classification, it is important to note that real-time audios are dependent on previous scenes. Also, the main drawback of deep learning algorithms is that they need a huge number of datasets to indicate their efficient performance.

In this letter, we propose a novel linguistic steganographic method that directly conceals a token-level secret message in a seemingly-natural steganographic text generated by the off-the-shelf BERT model equipped with Gibbs sampling. Compared with all modification based linguistic steganographic methods, the proposed method does not modify a given cover text. Instead, the proposed method utilizes the secret message to directly generate the steganographic text.

Discriminative correlation filter (DCF)-based methods applied for UAV object tracking have received widespread attention due to their high efficiency. However, these methods are usually troubled by the boundary effect. Besides, the violent environment variations severely confuse trackers that neglect temporal environmental changes among consecutive frames, leading to unwanted tracking drift. In this letter, we propose a novel DCF-based tracking method to promote the insensitivity of the tracker under uncertain environmental changes.

Audio-guided face reenactment aims to generate authentic target faces that have matched facial expression of the input audio, and many learning-based methods have successfully achieved this. However, most methods can only reenact a particular person once trained or suffer from the low-quality generation of the target images. Also, nearly none of the current reenactment works consider the model size and running speed that are important for practical use.

In this paper, we propose an enhancing steganographic scheme by random generation and ensemble stego selection. Different from existing steganography that only focuses on distortion function designing, our scheme considers both distortion model and optimized stego generation. In specific, for given cover, we firstly train an universal steganalyzer to calculate its gradient map, which is referenced to randomly adjust cost distribution of this cover. 


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