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Added by
Dahuin Jung
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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It is difficult to detect and remove secret images that are hidden in natural images using deep-learning algorithms. Our technique is the first work to effectively disable covert communications and transactions that use deep-learning steganography. We address the problem by exploiting sophisticated pixel distributions and edge areas of images using a deep neural network. Based on the given information, we adaptively remove secret information at the pixel level. We also introduce a new quantitative metric called destruction rate since the decoding method of deep-learning steganography is approximate (lossy), which is different from conventional steganography. We evaluate our technique using three public benchmarks in comparison with conventional steganalysis methods and show that the decoding rate improves by 10 ~ 20%.
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Steganography is the science of unnoticeably concealing a secret message within a certain image, called a cover image. The cover image with the secret message is called a stego image. Steganography is commonly used for illegal purposes such as terrorist activities and pornography. To thwart covert communications and transactions, attacking algorithms against steganography, called steganalysis, exist. Currently, there are many studies implementing deep learning to the steganography algorithm. However, conventional steganalysis is no longer effective for deep learning based steganography algorithms. Our framework is the first one to disturb covert communications and transactions via the recent deep learning-based steganography algorithms. We first extract a sophisticated pixel distribution of the potential stego image from the auto-regressive model induced by deep learning. Using the extracted pixel distributions, we detect whether an image is the stego or not at the pixel level. Each pixel value is adjusted as required and the adjustment induces an effective removal of the secret image. Because the decoding method of deep learning-based steganography algorithms is approximate (lossy), which is different from the conventional steganography, we propose a new quantitative metric that is more suitable for measuring the accurate effect. We evaluate our method using three public benchmarks in comparison with a conventional steganalysis method and show up to a 20% improvement in terms of decoding rate.
High-efficiency video coding (HEVC) encryption has been proposed to encrypt syntax elements for the purpose of video encryption. To achieve high video security, to the best of our knowledge, almost all of the existing HEVC encryption algorithms mainly encrypt the whole video, such that the user without permissions cannot obtain any viewable information. However, these encryption algorithms cannot meet the needs of customers who need part of the information but not the full information in the video. In many cases, such as professional paid videos or video meetings, users would like to observe some visible information in the encrypted video of the original video to satisfy their requirements in daily life. Aiming at this demand, this paper proposes a multi-level encryption scheme that is composed of lightweight encryption, medium encryption and heavyweight encryption, where each encryption level can obtain a different amount of visual information. It is found that both encrypting the luma intraprediction model (IPM) and scrambling the syntax element of the DCT coefficient sign can achieve the performance of a distorted video in which there is still residual visual information, while encrypting both of them can implement the intensity of encryption and one cannot gain any visual information. The experimental results meet our expectations appropriately, indicating that there is a different amount of visual information in each encryption level. Meanwhile, users can flexibly choose the encryption level according to their various requirements.
In this paper, a new framework for construction of Cardan grille for information hiding is proposed. Based on the semantic image inpainting technique, the stego image are driven by secret messages directly. A mask called Digital Cardan Grille (DCG) for determining the hidden location is introduced to hide the message. The message is written to the corrupted region that needs to be filled in the corrupted image in advance. Then the corrupted image with secret message is feeded into a Generative Adversarial Network (GAN) for semantic completion. The adversarial game not only reconstruct the corrupted image , but also generate a stego image which contains the logic rationality of image content. The experimental results verify the feasibility of the proposed method.
Video question answering is a challenging task, which requires agents to be able to understand rich video contents and perform spatial-temporal reasoning. However, existing graph-based methods fail to perform multi-step reasoning well, neglecting two properties of VideoQA: (1) Even for the same video, different questions may require different amount of video clips or objects to infer the answer with relational reasoning; (2) During reasoning, appearance and motion features have complicated interdependence which are correlated and complementary to each other. Based on these observations, we propose a Dual-Visual Graph Reasoning Unit (DualVGR) which reasons over videos in an end-to-end fashion. The first contribution of our DualVGR is the design of an explainable Query Punishment Module, which can filter out irrelevant visual features through multiple cycles of reasoning. The second contribution is the proposed Video-based Multi-view Graph Attention Network, which captures the relations between appearance and motion features. Our DualVGR network achieves state-of-the-art performance on the benchmark MSVD-QA and SVQA datasets, and demonstrates competitive results on benchmark MSRVTT-QA datasets. Our code is available at https://github.com/MMIR/DualVGR-VideoQA.
Existing vision systems for autonomous driving or robots are sensitive to waterdrops adhered to windows or camera lenses. Most recent waterdrop removal approaches take a single image as input and often fail to recover the missing content behind waterdrops faithfully. Thus, we propose a learning-based model for waterdrop removal with stereo images. To better detect and remove waterdrops from stereo images, we propose a novel row-wise dilated attention module to enlarge attentions receptive field for effective information propagation between the two stereo images. In addition, we propose an attention consistency loss between the ground-truth disparity map and attention scores to enhance the left-right consistency in stereo images. Because of related datasets unavailability, we collect a real-world dataset that contains stereo images with and without waterdrops. Extensive experiments on our dataset suggest that our model outperforms state-of-the-art methods both quantitatively and qualitatively. Our source code and the stereo waterdrop dataset are available at href{https://github.com/VivianSZF/Stereo-Waterdrop-Removal}{https://github.com/VivianSZF/Stereo-Waterdrop-Removal}
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