An integral part of video analysis and surveillance is temporal activity detection, which means to simultaneously recognize and localize activities in long untrimmed videos. Currently, the most effective methods of te...
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In recent years,binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information *** most state-of-the-art binary image steganographic schemes,t...
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In recent years,binary image steganography has developed so rapidly that the research of binary image steganalysis becomes more important for information *** most state-of-the-art binary image steganographic schemes,they always find out the flippable pixels to minimize the embedding *** this reason,the stego images generated by the previous schemes maintain visual quality and it is hard for steganalyzer to capture the embedding trace in spacial ***,the distortion maps can be calculated for cover and stego images and the difference between them is *** this paper,a novel binary image steganalytic scheme is proposed,which is based on distortion level co-occurrence *** proposed scheme first generates the corresponding distortion maps for cover and stego *** the co-occurrence matrix is constructed on the distortion level maps to represent the features of cover and stego ***,support vector machine,based on the gaussian kernel,is used to classify the *** with the prior steganalytic methods,experimental results demonstrate that the proposed scheme can effectively detect stego images.
With the emergence of varieties of audio editing software, disguised voices can be generated easily and are able to spoof the automatic speaker verification system. In this work, we focus on the spoofed voice disguise...
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In this paper, we propose a statistical learning aided list decoding algorithm, which integrates a serial list Viterbi algorithm (SLVA) with a soft check instead of the conventional cyclic redundancy check (CRC), for ...
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In this paper, we propose a privacy-preserving medical treatment system using nondeterministic finite automata (NFA), hereafter referred to as P-Med, designed for the remote medical environment. P-Med makes use of the...
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In this paper, we formulate a new problem to cope with the transmission of extra bits over an existing coded transmission link (referred to as coded payload link) without any cost of extra transmission energy or extra...
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A challenging task in the field of multimedia security involves concealing or eliminating the traces left by a chain of multiple manipulating operations, i.e., multiple-operation anti-forensics in short. However, the ...
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ISBN:
(数字)9781728132488
ISBN:
(纸本)9781728132495
A challenging task in the field of multimedia security involves concealing or eliminating the traces left by a chain of multiple manipulating operations, i.e., multiple-operation anti-forensics in short. However, the existing anti-forensic works concentrate on one specific manipulation, referred as single-operation anti-forensics. In this work, we propose using the improved Wasserstein generative adversarial networks with gradient penalty (WGAN-GP) to model image anti-forensics as an image-to-image translation problem and obtain the optimized anti-forensic models of multiple-operation. The experimental results demonstrate that our multiple-operation anti-forensic scheme successfully deceives the state-of-the-art forensic algorithms without significantly degrading the quality of the image, and even enhancing quality in most cases. To our best knowledge, this is the first attempt to explore the problem of multiple-operation anti-forensics.
We study the problem of leveraging the syntactic structure of text to enhance pre-trained models such as BERT and RoBERTa. Existing methods utilize syntax of text either in the pre-training stage or in the fine-tuning...
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Bitcoin is the most popular decentralized digital currency now in use. Block chain is the basic technology of Bitcoin, providing a trustable ledger that can be publicly verified. Research on distributed applications b...
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General-purpose forensics on small image patches appears to be feasible and important, but in fact poses a challenge due to insufficient statistics. Furthermore, there is a need to develop a forensic approach that can...
General-purpose forensics on small image patches appears to be feasible and important, but in fact poses a challenge due to insufficient statistics. Furthermore, there is a need to develop a forensic approach that can automatically learn effective and robust features related to image forensics with high parameter efficiency. In this paper, we propose a depthwise separable convolutional neural network (CNN) for the simultaneous detection of eleven types of image manipulations in image patches. Different from the previous CNNs based on standard convolution, depthwise separable convolution is introduced in the proposed CNN to adaptively extract forensics-related features from image patches with better parameter efficiency. When compared with four state-of-the-art methods, experiments demonstrate that the proposed CNN architecture can achieve better performance, e.g., the improvement in terms of accuracy in the detection of 32 × 32 images is up to 7.33%. It also achieves significantly better overall performance for different databases and better robustness against JPEG compression.
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