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Improving Transferability Reversible Adversarial Examples Based on Flipping Transformation 9th

Improving Transferability Reversible Adversarial Examples Ba...

引用

9th International conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2023

作者： Fang, Youqing Jia, Jingwen Yang, Yuhai Lyu, Wanli Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China

ISBN: (纸本)9789819959679

Adding subtle perturbations to an image can cause the classification model to misclassify, and such images are called adversarial examples. Adversarial examples threaten the safe use of deep neural networks, but when combined with reversible data hiding (RDH) technology, they can protect images from being correctly identified by unauthorized models and recover the image lossless under authorized models. Based on this, the reversible adversarial example (RAE) is rising. However, existing RAE technology focuses on feasibility, attack success rate and image quality, but ignores transferability and time complexity. In this paper, we optimize the data hiding structure and combine data augmentation technology, which flips the input image in probability to avoid overfitting phenomenon on the dataset. On the premise of maintaining a high success rate of white-box attacks and the image’s visual quality, the proposed method improves the transferability of reversible adversarial examples by approximately 16% and reduces the computational cost by approximately 43% compared to the state-of-the-art method. In addition, the appropriate flip probability can be selected for different application scenarios. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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AsymLLIC: Asymmetric Lightweight Learned image Compression

AsymLLIC: Asymmetric Lightweight Learned Image Compression

引用

IEEE visual communications and image processing (VCIP)

作者： Shen Wang Zhengxue Cheng Donghui Feng Guo Lu Li Song Wenjun Zhang Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Learned image compression (LIC) methods often employ symmetrical encoder and decoder architectures, evitably increasing decoding time. However, practical scenarios demand an asymmetric design, where the decoder requires low complexity to cater to diverse low-end devices, while the encoder can accommodate higher complexity to improve coding performance. In this paper, we propose an asymmetric lightweight learned image compression (AsymLLIC) architecture with a novel training scheme, enabling the gradual substitution of complex decoding modules with simpler ones. Building upon this approach, we conduct a comprehensive comparison of different decoder network structures to strike a better trade-off between complexity and compression performance. Experiment results validate the efficiency of our proposed method, which not only achieves comparable performance to VVC but also offers a lightweight decoder with only 51.47 GMACs computation and 19.65M parameters. Furthermore, this design methodology can be easily applied to any LIC models, enabling the practical deployment of LIC techniques.

关键词： Training Performance evaluation image coding visual communication Design methodology Computational modeling Computer architecture Decoding Complexity theory Computational efficiency

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Learned image Compression With Variable Neural Network Architecture

Learned Image Compression With Variable Neural Network Archi...

引用

IEEE visual communications and image processing (VCIP)

作者： Ziyi Zeng Lu Yu Zhejiang University Hangzhou China

With the development of neural networks, the coding efficiency of learned image compression methods gradually exceeds that of traditional image codecs that are carefully designed and optimized by experts. However, the deep image compression model with an invariable network structure and fixed network parameters can only cover a limited range of bit rates. Using the same network structure for different bit rate ranges leads to sub-optimal coding efficiency. Meanwhile, the more complex network structure and the increase of network parameters cause higher model complexity, which makes it difficult for network training and practical application. In this paper, a new perspective is proposed to solve the above challenges. The idea of variable network architecture is introduced to the field of learned image compression. We devise two strategies with variable network architecture, which improve the coding performance of the learned image compression model from the coding efficiency and complexity respectively. Compared to the image compression method of fixed network structure, our first approach attains an average BDPSNR of 0.771dB and an average BDBR of −14.876%. The second attains an average BDPSNR of 0.320dB and an average BDBR of −5.471%. These results demonstrate the effectiveness of variable network structure in improving coding efficiency and controlling model complexity.

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visual Question Answering

Visual Question Answering

引用

International conference on Computing, Networking and communications (ICNC)

作者： Ahmed Nada Min Chen Computing and Software Systems University of Washington Bothell Bothell USA

ISBN: (数字)9798350370997

ISBN: (纸本)9798350371000

visual question answering (VQA) is an artificial intelligence (AI) and computer vision (CV) comprehensive task to answer questions about the visual content of an image, such as “what color is the bus?” or “how many people are in the photo?” VQA has shown great potential and importance in various domains, ranging from medical imaging applications, autonomous driving, to virtual assistants and search engines. This study develops a framework to tackle VQA research challenges by adopting and extending recent breakthroughs in attention techniques, natural language processing, and image classification models. In addition, different from other previous work that uses static question embedding, we investigate how alternative dynamic embedding models enhance the effectiveness of VQA task. The work is evaluated using the latest developed VQA v2 dataset with a 9% improvement over the results obtained with static word embedding. We also deployed the model as a cloud based VQA system to facilitate VQA tasks in real-life applications.

关键词： visualization Computer vision image coding Computational modeling Virtual assistants Optical character recognition Search engines

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A Practical Approach to Depth-Aware Augmentation for Neural Radiance Fields

A Practical Approach to Depth-Aware Augmentation for Neural ...

引用

IEEE visual communications and image processing (VCIP)

作者： Hamed Razavi Khosroshahi Jaime Sancho Daniele Bonatto Sarah Fachada Gun Bang Gauthier Lafruit Eduardo Juarez Mehrdad Teratani Laboratory of Image Synthesis and Analysis Universite Libre de Bruxelles Brussels Belgium Research Center on Software Technologies and Multimedia Systems Universidad Politecnica de Madrid Madrid Spain Electronics Telecommunication Research Institute Daejeon Korea

ISBN: (数字)9798331529543

ISBN: (纸本)9798331529550

Neural Radiance Fields (NeRF) have demonstrated exceptional performance in generating novel views of scenes by learning implicit volumetric representations from calibrated RGB images, without depth information. A major limitation is the need for large training datasets in neural network-based view synthesis frameworks. The challenge of effective data augmentation for view synthesis remains unresolved. NeRF models require extensive scene coverage from multiple views to accurately estimate radiance and density. Insufficient coverage reduces the model’s ability to interpolate or extrapolate unseen parts of the scene effectively. In this paper, we propose a novel pipeline to address this data augmentation issue using depth map information. We use depth image-based rendering (DIBR) to overcome the lack of enough views for training NeRF. Experimental results indicate that our approach enhances the quality of rendered images using the NeRF framework, achieving an average peak signal-to-noise ratio (PSNR) increase of 7.2 dB, with a maximum improvement of 12 dB.

关键词： Training Measurement PSNR visual communication image processing Pipelines Neural radiance field Data augmentation Rendering (computer graphics) Software

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Microscopic Fluorescence In Situ Hybridization (FISH) image Synthesis with Generative Adversarial Networks 29

Microscopic Fluorescence In Situ Hybridization (FISH) Image ...

引用

29th IEEE conference on Signal processing and communications Applications (SIU)

作者： Dursun, Gizem Ozkaya, Ufuk Suleyman Demirel Univ Elekt Elekt Muhendisligi Isparta Turkey

ISBN: (纸本)9781665436496

One of the most important problems in biomedical image analysis is the low amount of data and the cost of accessing to the marked data by researchers. In order to provide a solution to this problem, microscopic fluorescence in situ hybridization (FISH) images are synthesized with generative adversarial network in this paper. The generative adversarial network is trained to synthesize FISH images from mask images. The trained model was implemented on 150 test images and the performance of the model both was presented with visual results and evaluated quantitatively by calculating the performance metrics. By evaluating the synthesized FISH images in terms of image quality and structural features, it is observed that they can be used to provide a solution to the problem of the lack of data.

关键词： generative adversarial networks image synthesis fluorescence in situ hybridization

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ARITM: A Perceptual Quality Magnification of Noise Suppressed Digital images using Adaptive Recursion and image Transformation Model

ARITM: A Perceptual Quality Magnification of Noise Suppresse...

引用

2023 International conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Kannan, K. Divya, G. Chithra, L. Aruna, V. Mohanavel, V. College of Engineering and Technology Puduvoyal Department of ECER.M.K. Chennai India SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Networking and Communications Chennai India S.A.Engineering College Department of ECE Chennai India Panimalar Engineering College Department of ECE Chennai India Bharath Institute of Higher Education and Research Centre for Materials Engineering and Regenerative Medicine Tamil Nadu Chennai India Chandigarh University Department of Mechanical Engineering Punjab Mohali140413 India

ISBN: (纸本)9798350319200

In the realm of digital image processing and enhancement, the relentless pursuit of achieving higher perceptual quality in noise-suppressed images has remained a compelling challenge. This abstract presents a novel approach that leverages the synergy between adaptive recursion and a sophisticated image transformation model to magnify the perceptual quality of noise-suppressed digital images. The proliferation of digital imagery in various applications, from medical diagnostics to multimedia content, necessitates the development of advanced techniques to preserve and enhance image fidelity. While noise suppression techniques have made significant strides in reducing unwanted artifacts, they often introduce trade-offs between noise reduction and preservation of image details. Our proposed method begins by employing adaptive recursion, where the image is divided into local regions, each analyzed independently for noise characteristics and visual features. Adaptive recursion ensures that noise reduction is applied selectively, preserving important image details, even in challenging scenarios. The synergy between adaptive recursion and the image transformation model is a key highlight of our approach. By iteratively applying the transformation model within each local region, we adaptively enhance image features that contribute to perceptual quality, such as sharpness, contrast, and color fidelity. This adaptive enhancement process ensures that image details are magnified while suppressing noise artifacts, ultimately resulting in images of superior perceptual quality. Our experimental results, conducted on diverse datasets with various levels of noise, demonstrate significant improvements in perceptual quality compared to state-of-the-art methods. Additionally, we evaluate the computational efficiency of our approach, highlighting its feasibility for real-time and high-throughput image enhancement applications. © 2023 IEEE.

关键词： Adaptive Recursion Generative Adversarial Networks (GANs) Noise-suppressed images

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Filling the Gap: Enhancing Ultra-Low Light image Brightness Through Multi-Band NIR Estimation

Filling the Gap: Enhancing Ultra-Low Light Image Brightness ...

引用

IEEE visual communications and image processing (VCIP)

作者： Jeong-Hyeok Park Tae-Sung Park Jong-Ok Kim School of Electrical Engineering Korea University Seoul Korea

We propose a method for capturing high-quality images in low-light environments using multi-band near-infrared (NIR) images, which offer robustness to brightness variations and provide structural information not present in RGB images. As multi-band NIR images are not commonly available in consumer cameras, an RGB-NIR conversion network is employed to estimate them. Training data comes from a new low-light dataset with NIR-RGB image pairs. Unlike single-band approaches, this method utilizes multi-band NIR images with peak wavelengths of 785nm, 850nm, and 940nm, resulting in enhanced quality. Integration of the RGB-NIR conversion network into an existing low-light enhancement (LLIE) network is achieved using the proposed cross-attention transformer. Experimental results validate the effectiveness of multi-band NIR estimation in enhancing low-light intensity. Moreover, the proposed RGB-NIR conversion network and cross-attention module can be applied to any existing deep LLIE networks.

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PTS-LIC: Pruning Threshold Searching for Lightweight Learned image Compression

PTS-LIC: Pruning Threshold Searching for Lightweight Learned...

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IEEE visual communications and image processing (VCIP)

作者： Ao Luo Heming Sun Jinming Liu Fangzheng Lin Jiro Katto Department of Computer Science and Communication Engineering Waseda University Tokyo Japan Faculty of Engineering Yokohama National University Kanagawa Japan Shanghai Jiao Tong University China Waseda Research Institute for Science and Engineering Tokyo Japan

Learned image Compression (LIC), which uses neural networks to compress images, has experienced significant growth in recent years. The hyperprior-module-based LIC model has achieved higher performance than classical codecs. However, the LIC models are too heavy (in calculation and parameter amounts) to apply to edge devices. To solve this problem, some former papers focus on structural pruning for LIC models. However, they either cause noticeable performance decrement or neglect the appropriate pruning threshold for each LIC model. These problems keep their pruning results sub-optimal. This paper proposes a Pruning Threshold Searching on the hyperprior module for different-quality LIC models. Our method removes most parameters and calculations while keeping the performance the same as the models before pruning. We removed at least 49.8% of parameters and 28.5% of calculations for the Channel-Wise-Context-Model-based models and 29.1% of parameters for the Cheng-2020 models.

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Portable Ticket Automatic Scanning Technology Based on Lightweight visual Recognition

Portable Ticket Automatic Scanning Technology Based on Light...

引用

Electronic communications, Internet of Things and Big Data (ICEIB), International conference on

作者： Fangyi Zhao Yuekang Yu Fan Yang School of Information and Communication Guilin University of Electronic Technology Guilin China Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

In accounting and financial management, bills serve as vouchers for enterprises and government departments. The processing of bill information is fundamental to financial management with importance. We created a portable bill-scanning technique based on lightweight visual recognition. An image acquisition module was used to capture bill images, which are transmitted to a microprocessor. The microprocessor was used to grayscale the images using an improved Canny edge detection operator for bill edge scanning and correct the images using Hough transformation. Then, convolutional neural networks were utilized for financial data extraction, yielding high-quality financial data. The proposed bill-scanning method effectively reduced false alarm rates while maintaining high recall rates, addressing prevalent shortcomings in current bill-scanning technologies.

关键词： visualization image segmentation Power demand image edge detection Microprocessors Financial management Gray-scale

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