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检索条件"任意字段=2021 International Conference on Visual Communications and Image Processing, VCIP 2021"

共 3162 条记录，以下是31-40 订阅

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A Deep Learning-based Audio-in-image Watermarking Scheme

A Deep Learning-based Audio-in-Image Watermarking Scheme

引用

IEEE international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Das, Arjon Zhong, Xin Univ Nebraska Omaha Dept Comp Sci Omaha NE 68182 USA

ISBN: (纸本)9781728185514

This paper presents a deep learning-based audio-in-image watermarking scheme. Audio-in-image watermarking is the process of covertly embedding and extracting audio watermarks on a cover-image. Using audio watermarks can open up possibilities for different downstream applications. For the purpose of implementing an audio-in-image watermarking that adapts to the demands of increasingly diverse situations, a neural network architecture is designed to automatically learn the watermarking process in an unsupervised manner. In addition, a similarity network is developed to recognize the audio watermarks under distortions, therefore providing robustness to the proposed method. Experimental results have shown high fidelity and robustness of the proposed blind audio-in-image watermarking scheme.

关键词： Audio-in-image watermarking deep learning neural networks robustness

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Alpha-trimmed Mean Filter and XOR based image Enhancement for Embedding Data in image

Alpha-trimmed Mean Filter and XOR based Image Enhancement fo...

引用

IEEE international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Alam, S. M. Ammar Ul Huda, Moid Farhan, Muhammad Habib Univ Dhanani Sch Sci & Engr Karachi Pakistan

ISBN: (纸本)9781728185514

In the age of digital content creation and distribution, steganography, that is, hiding of secret data within another data is needed in many applications, such as in secret communication between two parties, piracy protection, etc. In image steganography, secret data is generally embedded within the image through an additional step after a mandatory image enhancement process. In this paper, we propose the idea of embedding data during the image enhancement process. This saves the additional work required to separately encode the data inside the cover image. We used the Alpha-Trimmed mean filter for image enhancement and XOR of the 6 MSBs for embedding the two bits of the bitstream in the 2 LSBs whereas the extraction is a reverse process. Our obtained quantitative and qualitative results are better than a methodology presented in a very recent paper.

关键词： steganography alpha-trimmed mean LSB manipulation image enhancement data embedding

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Scalable Privacy in Multi-Task image Compression

Scalable Privacy in Multi-Task Image Compression

引用

IEEE international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Alvar, Saeed Ranjbar Bajic, Ivan, V Simon Fraser Univ Sch Engn Sci Burnaby BC V5A 1S6 Canada

ISBN: (纸本)9781728185514

Learning-based compression systems have shown great potential for multi-task inference from their latent-space representation of the input image. In such systems, the decoder is supposed to be able to perform various analyses of the input image, such as object detection or segmentation, besides decoding the image. At the same time, privacy concerns around visual analytics have grown in response to the increasing capabilities of such systems to reveal private information. In this paper, we propose a method to make latent-space inference more privacy-friendly using mutual information-based criteria. In particular, we show how organizing and compressing the latent representation of the image according to task-specific mutual information can make the model maintain high analytics accuracy while becoming less able to reconstruct the input image and thereby reveal private information.

关键词： Learning-based compression privacy mutual information

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Towards Universal GAN image Detection

Towards Universal GAN Image Detection

引用

IEEE international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Cozzolino, Davide Gragnaniello, Diego Poggi, Giovanni Verdoliva, Luisa Univ Federico II Naples Naples Italy

ISBN: (纸本)9781728185514

The ever higher quality and wide diffusion of fake images have spawn a quest for reliable forensic tools. Many GAN image detectors have been proposed, recently. In real world scenarios, however, most of them show limited robustness and generalization ability. Moreover, they often rely on side information not available at test time, that is, they are not universal. We investigate these problems and propose a new GAN image detector based on a limited sub-sampling architecture and a suitable contrastive learning paradigm. Experiments carried out in challenging conditions prove the proposed method to be a first step towards universal GAN image detection, ensuring also good robustness to common image impairments, and good generalization to unseen architectures.

关键词： Fake detection

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AnomalyHop: An SSL-based image Anomaly Localization Method

AnomalyHop: An SSL-based Image Anomaly Localization Method

引用

IEEE international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Zhang, Kaitai Wang, Bin Wang, Wei Sohrab, Fahad Gabbouj, Moncef Kuo, C-C Jay Univ Southern Calif Ming Hsieh Dept Elect & Comp Engn Los Angeles CA 90007 USA Tampere Univ Dept Comp Sci Tampere Finland

ISBN: (纸本)9781728185514

An image anomaly localization method based on the successive subspace learning (SSL) framework, called AnomalyHop, is proposed in this work. AnomalyHop consists of three modules: 1) feature extraction via successive subspace learning (SSL), 2) normality feature distributions modeling via Gaussian models, and 3) anomaly map generation and fusion. Comparing with state-of-the-art image anomaly localization methods based on deep neural networks (DNNs), AnomalyHop is mathematically transparent, easy to train, and fast in its inference speed. Besides, its area under the ROC curve (ROC-AUC) performance on the MVTec AD dataset is 95.9%, which is among the best of several benchmarking methods.

关键词： image anomaly localization successive subspace learning

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Parallelized Context Modeling for Faster image Coding

Parallelized Context Modeling for Faster Image Coding

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IEEE international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Koyuncu, A. Burakhan Cui, Kai Boev, Atanas Steinbach, Eckehard Tech Univ Munich Chair Media Technol Munich Germany Huawei Munich Res Ctr Audiovideo Technol Lab Munich Germany

ISBN: (纸本)9781728185514

Learning-based image compression has reached the performance of classical methods such as BPG. One common approach is to use an autoencoder network to map the pixel information to a latent space and then approximate the symbol probabilities in that space with a context model. During inference, the learned context model provides symbol probabilities, which are used by the entropy encoder to obtain the bitstream. Currently, the most effective context models use autoregression, but autoregression results in a very high decoding complexity due to the serialized data processing. In this work, we propose a method to parallelize the autoregressive process used for image compression. In our experiments, we achieve a decoding speed that is over 8 times faster than the standard autoregressive context model almost without compression performance reduction.

关键词： image compression

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No-Reference Stereoscopic image Quality Assessment Considering Binocular Disparity and Fusion Compensation

No-Reference Stereoscopic Image Quality Assessment Consideri...

引用

IEEE international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Feng Jinhui Li, Sumei Chang, Yongli Tianjin Univ Tianjin Int Engn Inst Tianjin Peoples R China Tianjin Univ Sch Elect & Informat Engn Tianjin Peoples R China

ISBN: (纸本)9781728185514

In this paper, we propose an optimized dual stream convolutional neural network (CNN) considering binocular disparity and fusion compensation for no-reference stereoscopic image quality assessment (SIQA). Different from previous methods, we extract both disparity and fusion features from multiple levels to simulate hierarchical processing of the stereoscopic images in human brain. Given that the ocular dominance plays an important role in quality evaluation, the fusion weights assignment module (FWAM) is proposed to assign weight to guide the fusion of the left and the right features respectively. Experimental results on four public stereoscopic image databases show that the proposed method is superior to the state-of-the-art SIQA methods on both symmetrical and asymmetrical distortion stereoscopic images.

关键词： stereoscopic image quality assessment dual stream ocular dominance binocular disparity binocular fusion

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GPU-Intensive Fast Entropy Coding Framework for Neural image Compression

GPU-Intensive Fast Entropy Coding Framework for Neural Image...

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IEEE international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Akutsu, Hiroaki Naruko, Takahiro Suzuki, Akifumi Hitachi Ltd Res & Dev Grp Tokyo Japan

ISBN: (纸本)9781728185514

Neural compression has benefited from technological advances such as convolutional neural networks (CNNs) to achieve advanced bitrates, especially in image compression. In neural image compression, an encoder and a decoder can run in parallel on a GPU, so the speed is relatively fast. However, the conventional entropy coding for neural image compression requires serialized iterations in which the probability distribution is estimated by multi-layer CNNs and entropy coding is processed on a CPU. Therefore, the total compression and decompression speed is slow. We propose a fast, practical, GPU-intensive entropy coding framework that consistently executes entropy coding on a GPU through highly parallelized tensor operations, as well as an encoder, decoder, and entropy estimator with an improved network architecture. We experimentally evaluated the speed and rate-distortion performance of the proposed framework and found that we could significantly increase the speed while maintaining the bitrate advantage of neural image compression.

关键词： neural image compression entropy coding GPU

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Fast and Context-Aware Framework for Space-Time Video Super-Resolution

Fast and Context-Aware Framework for Space-Time Video Super-...

引用

IEEE international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Zhang, Xueheng Chen, Li Song, Li Gao, Zhiyong Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China

ISBN: (纸本)9781728185514

Increasing the spatial resolution and frame rate of a video simultaneously has attracted attention in recent years. The current one-stage space-time video super-resolution (STVSR) methods are difficult to deal with large motion and complex scenes, and are time-consuming and memory intensive. We propose an efficient STVSR framework, which can correctly handle complicated scenes such as occlusion and large motion and generate results with clearer texture. In REDS dataset, our method outperforms all existing one-stage methods. Our method is lightweight and can generate 720p frames at 16fps on a NVIDIA GTX 1080 Ti GPU.

关键词： space-time video super-resolution frame interpolation deep learning

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Security and Forensics Exploration of Learning-based image Coding

Security and Forensics Exploration of Learning-based Image C...

引用

IEEE international conference on visual communications and image processing (vcip) - visual communications in the Era of AI and Limited Resources

作者： Bhowmik, Deepayan Elawady, Mohamed Nogueira, Keiller Univ Stirling Div Comp Sci & Math Stirling FK9 4LA Scotland

ISBN: (纸本)9781728185514

Advances in media compression indicate significant potential to drive future media coding standards, e.g., Joint Photographic Experts Group's learning-based image coding technologies (JPEG AI) and Joint Video Experts Team's (JVET) deep neural networks (DNN) based video coding. These codecs in fact represent a new type of media format. As a dire consequence, traditional media security and forensic techniques will no longer be of use. This paper proposes an initial study on the effectiveness of traditional watermarking on two state-of-the-art learning based image coding. Results indicate that traditional watermarking methods are no longer effective. We also examine the forensic trails of various DNN architectures in the learning based codecs by proposing a residual noise based source identification algorithm that achieved 79% accuracy.

关键词： Media forensics security learning based image coding JPEG AI DNN watermarking source identification

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