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image Steganography: Uncovering Hiding image Using Deep Learning for Attention-Generative Adversarial Network

Image Steganography: Uncovering Hiding Image Using Deep Lear...

引用

Mobile Networks and Wireless communications (ICMNWC), ieee International conference on

作者： Manjula B. M N Shilpa Zainab Alassedi Zainab abed Almoussawi Aboothar mahmood Shakir Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of ECE School of Engineering SR University Warangal Telangana India National University of Science and Technology Dhi Qar Iraq College of Islamic Science Ahl Al Bayt University karbala Iraq The Islamic University Najaf Iraq

Steganography is one of the most crucial role in the field of information hiding, which is the technique to hiding a secrete image from ordinary file or message in order to avoiding the detection of steganography models and human eye. In recent years, every scholars have various deep learning networks to the fields of steganography to improving accuracy of detection. In this paper propose Attention- Generative Adversarial Network (GAN) has two aspects: Architecture of Network and Loss function. Architecture network consists of three networks: encoder network, decoder network and discriminator network. Encoder has a hiding the secrete color image into a cover color image with the same size to generate the steganography image from a visual quality by encoder network. Secrete image are extracted by the decoder network. Discriminator taking the cover image and stego image as input to classify the whether the input image contain the secrete image. Finally, Loss function, they are optimizing the weight of the encoder and decoder network through an adversarial training model. The results obtained DL using Attention-GAN show a high PSNR was improved to 44.60, SSIM of 0.998, and MSE of 103.26 on imageNet dataset, which ensure GAN compared to other exiting methods like Light-weight method, Deep Adaptive Hiding Network (DAH-Net) and Transformer Recursive Permutation (TRP) stego method.

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Transformer Reasoning Network for image-Text Matching and Retrieval 25

Transformer Reasoning Network for Image-Text Matching and Re...

引用

25th International conference on Pattern Recognition (ICPR)

作者： Messina, Nicola Falchi, Fabrizio Esuli, Andrea Amato, Giuseppe CNR Inst Informat Sci & Technol Pisa Italy

ISBN: (纸本)9781728188089

image-text matching is an interesting and fascinating task in modern Al research. Despite the evolution of deep-learning-based image and text processing systems, multi-modal matching remains a challenging problem. In this work, we consider the problem of accurate image-text matching for the task of multi-modal large-scale information retrieval. State-of-the-art results in image-text matching are achieved by interplaying image and text features from the two different processing pipelines, usually using mutual attention mechanisms. However, this invalidates any chance to extract separate visual and textual features needed for later indexing steps in large-scale retrieval systems. In this regard, we introduce the Transformer Encoder Reasoning Network (TERN), an architecture built upon one of the modern relationship-aware self-attentive architectures, the Transformer Encoder (TE). This architecture is able to separately reason on the two different modalities and to enforce a final common abstract concept space by sharing the weights of the deeper transformer layers. Thanks to this design, the implemented network is able to produce compact and very rich visual and textual features available for the successive indexing step. Experiments are conducted on the MS-COCO dataset, and we evaluate the results using a discounted cumulative gain metric with relevance computed exploiting caption similarities, in order to assess possibly non-exact but relevant search results. We demonstrate that on this metric we are able to achieve state-of-the-art results in the image retrieval task. Our code is freely available at https://***/mesnicolTERN.

关键词： Deep learning

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Heuristic Motion Planning for Autonomous Outdoor Vehicle: A Lane Detection and Tracking Approach

Heuristic Motion Planning for Autonomous Outdoor Vehicle: A ...

引用

Student conference on Research and Development (SCOReD)

作者： Zunnurain Bin Zainor Wan Nurshazwani Wan Zakaria Mohamad Dzulhelmy Amari Abu Ubaidah Shamsudin School of Mechanical Engineering College of Engineering Universiti Teknologi MARA Shah Alam Malaysia Faculty of Electrical and Electronic Engineering Universiti Tun Hussein Onn Malaysia Batu Pahat Johor Malaysia

ISBN: (数字)9798331510077

ISBN: (纸本)9798331510084

The autonomous vehicle is a driverless vehicle that can travel from a starting point to a predetermined destination by detecting and responding to its local surroundings using various vehicle technologies and sensors. Planner modules provide collision-free waypoints on the route to the destination which serve as the foundation for the decision made by autonomous vehicles to avoid both static and dynamic obstacles. The aim of this project is to develop motion planning architecture for outdoor vehicles that can manoeuvre in indicated routes using visual based lane detection. An autonomous navigation system is developed based on image-based lane detection and heuristic algorithm to determine a suitable field of view for the vehicle to safely manoeuvre. The proposed algorithm was developed using ROS (Robot Operating System), with OpenCV tools employed for image processing. The TurtleBot 3 Waffle Pi model was utilized to evaluate the algorithm. The algorithm proposed it tested in 3 cases which are absent of obstacle, present of static, and present of dynamic objects. It was found that the results show that the vehicle can move autonomously with the successful rate of more than 95% in every case tested.

关键词： Lane detection Heuristic algorithms Roads Operating systems image processing image edge detection Dynamics Transforms Planning Vehicle dynamics

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Language-guided image Reflection Separation

Language-guided Image Reflection Separation

引用

conference on Computer Vision and Pattern Recognition (CVPR)

作者： Haofeng Zhong Yuchen Hong Shuchen Weng Jinxiu Liang Boxin Shi National Key Laboratory for Multimedia Information Processing School of Computer Science Peking University National Engineering Research Center of Visual Technology School of Computer Science Peking University AI Innovation Center School of Computer Science Peking University

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

This paper studies the problem of language-guided re-flection separation, which aims at addressing the ill-posed reflection separation problem by introducing language de-scriptions to provide layer content. We propose a unified framework to solve this problem, which leverages the cross-attention mechanism with contrastive learning strategies to construct the correspondence between language descriptions and image layers. A gated network design and a ran-domized training strategy are employed to tackle the rec-ognizable layer ambiguity. The effectiveness of the pro-posed method is validated by the significant performance advantage over existing reflection separation methods on both quantitative and qualitative comparisons.

关键词： Training Computer vision Annotations Natural languages Contrastive learning Logic gates Reflection

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Inclusive Mobility Solution for visually Impaired People using Google Cloud Vision

Inclusive Mobility Solution for Visually Impaired People usi...

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ieee International Smart Cities conference (ISC2)

作者： Goncalves, Joana Paiva, Sara Inst Politecn Viana do Castelo Viana Do Castelo Portugal Inst Politecn Viana do Castelo ADiT LAB Rua Escola Ind & Comercial NunAlvares P-4900347 Viana Do Castelo Portugal

ISBN: (纸本)9781665449199

Mobility in cities is of particular and growing importance nowadays due to the demographic increase and the existence of people with reduced mobility, as is the case of visually impaired people. Of the various situations where mobility represents a challenge, obtaining the notion of positioning, at times when the person loses track of where he is and becomes disoriented, can be extremely useful and a way to contribute to greater autonomy for this segment of people. This paper proposes a visual positioning system using the Google Cloud Vision API. The architecture includes a mobile application that captures an image via the mobile phone and sends it to a backend server that makes use of Google Cloud Vision to recognize the image, which may consist of text, logos or landmarks. In a first phase, the solution was evaluated individually and, in a second phase, on a route chosen in the city of Braga, in Portugal. Logo recognition achieved an accuracy of 98% and proved to be sensitive to image resolution. The frontal text recognition obtained an accuracy of 100% while the lateral recognition and at a 3 meters distance obtained lower values, with worse results in images with more text and of reduced dimensions. Landmark recognition always returned the correct result, although the average accuracy is 82%. The processing time was around 3 seconds in tests done with Wi-Fi network and about 2 seconds in field tests made with mobile network. The obtained results prove the adequacy of using this solution to be adapted in a real scenario.

关键词： visually Impaired People Mobility Inclusive Mobility Outdoor Positioning image Recognition

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Color image retrieval based on improved quaternion convolutional neural network 3

Color image retrieval based on improved quaternion convoluti...

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3rd International conference on Wireless communications and Smart Grid, ICWCSG 2021

作者： Wei, Weiyi Li, Lei Northwest Normal University College of Computer Science and Engineering Lanzhou China

ISBN: (纸本)9781665425988

Nowadays, when performing color image texture feature extraction, most of the color images are converted into grayscale images or the color images are processed by channels. The above-mentioned traditional processing methods will largely lose the effective information of the original image when performing color image retrieval. Aiming at the problem of effective feature description in content-based image retrieval, this paper proposes a color image retrieval method based on improved quaternion convolutional neural network. First, use the quaternion method to characterize the local texture feature LBP value and R, G, and B color information of the image, and use the quaternion discrete cosine transform (QDCT) as the theoretical basis to express the color image as a quaternion matrix form. Secondly, each component of the convolutional neural network is extended to the quaternion domain, and the quaternion matrix obtained above is directly input into the proposed neural network for training and classification. Finally, the result of image retrieval is obtained. Experiments show that this method significantly improves the performance of color image retrieval. © 2021 ieee.

关键词： Discrete cosine transforms

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Continuous Action Space-Based Spoken Language Acquisition Agent Using Residual Sentence Embedding and Transformer Decoder

Continuous Action Space-Based Spoken Language Acquisition Ag...

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Ryota Komatsu Yusuke Kimura Takuma Okamoto Takahiro Shinozaki Tokyo Institute of Technology National Institute of Information and Communications Technology

Studies on spoken language acquisition agents aim to understand the mechanism of human language learning and to realize it on computers. Existing open vocabulary agents first perform unsupervised word learning from speech signals to construct a word dictionary as a discrete action space and then conduct reinforcement learning to understand the use of the words in the dictionary through interaction with dialogue partners. A limitation is that they have difficulty pronouncing multi-word utterances. This study proposes an agent that generates multi-word waveform utterances using a continuous action space. The conventional agent uses a vision-focusing mechanism to accelerate dialogue-based learning by guiding the agent’s attention to those concepts in its eyesight. In contrast, the proposed agent replaces it with residual sentence embedding combined with vision features used as the action space. The agent consists of speech and image input front-ends, a transformer language model of pseudo-action space. Experimental results show that the agent learns multi-word utterances assisted by unsupervised learning algorithms using unlabeled speech and image data sets.

关键词： Vocabulary Dictionaries Synthesizers Signal processing algorithms Reinforcement learning Signal processing Transformers

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Multimodal Adversarial Defense Trained on Features Extracted from images and Brain Activity

Multimodal Adversarial Defense Trained on Features Extracted...

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ieee Global conference on Consumer Electronics (GCCE)

作者： Tasuku Nakajima Keisuke Maeda Ren Togo Takahiro Ogawa Miki Haseyama Hokkaido University Sapporo Hokkaido Japan

ISBN: (数字)9798350355079

ISBN: (纸本)9798350355086

Deep neural networks (DNNs) have achieved significant advancements in artificial intelligence (AI) but remain vulnerable to adversarial attacks. In contrast, the human cognitive system, integrating multimodal processing and brain activity, shows high robustness against such attacks. This paper introduces a novel multimodal adversarial defense method that incorporates brain activity data to enhance the DNN robustness. We use functional magnetic resonance imaging (fMRI) data recorded while subjects view images and combine it with visual data to create a more robust classifier. Experimental results using the NSD dataset demonstrate that our method improves robustness against adversarial attacks compared to traditional methods. This research underscores the potential of leveraging brain activity data to develop more robust AI systems.

关键词： Brain visualization Accuracy Artificial neural networks Functional magnetic resonance imaging Feature extraction Robustness Data models Data mining image classification

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Pulse rate variability estimation method based on imaging-photoplethysmography and application to telepsychiatry

Pulse rate variability estimation method based on imaging-ph...

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Annual International conference of the ieee Engineering in Medicine and Biology Society (EMBC)

作者： Maho Nishikawa Sumiyakhand Dagdanpurev Takuya Hashimoto Masaki Kurosawa Tetsuo Kirimoto Toshikazu Shinba Takemi Matsui Guanghao Sun Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo Japan National University of Mongolia Ulaanbaatar Mongolia Tokyo University of Science Tokyo Japan Department of Psychiatry Shizuoka Saiseikai General Hospital Shizuoka Japan Graduate School of System Design Tokyo Metropolitan University Tokyo Japan

The worldwide adoption of telehealth services may benefit people who otherwise would not be able to access mental health support. In this paper, we present a novel algorithm to obtain reliable pulse and respiration signals from non-contact facial image sequence analysis. The proposed algorithm involved a skin pixel extraction method in the image processing part and signal reconstruction using the spectral information of RGB signal in the signal processing part. The algorithm was tested on 15 healthy subjects in a laboratory setting. The results show that the proposed algorithm can accurately monitor respiration rate (RR), pulse rate (PR), and pulse rate variability (PRV) in rest *** Relevance— The main achievement of this study is enabling non-contact PR and RR signal extraction from facial image sequences, which has potential for future use and support for psychiatrists in telepsychiatry.

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Low-complexity Fast NLMS based on Partial Updating Strategy for Acoustic Echo Cancellation 2

Low-complexity Fast NLMS based on Partial Updating Strategy ...

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2nd International conference on Advanced Electrical Engineering, ICAEE 2022

作者： Ramdane, Mohamed Amine Benallal, Ahmed Maamoun, Mountassar Hassani, Islam University of Blida1 Route of Soumaa B.P.270 DetectionInformation and Communications Laboratory Blida09000 Algeria University of Blida1 Route of Soumaa B.P.270 Signal Processing and Image Laboratory Blida09000 Algeria

ISBN: (数字)9781665417419

ISBN: (纸本)9781665417419

For the purpose of reducing the computational load of the Fast Normalized Least Mean Square (FNLMS) algorithm for Acoustic Echo Cancellation (AEC) applications, we propose to incorporate the Partial Update strategy into the Fast NLMS algorithm to obtain a new algorithm that has less computational complexity and fast convergence rate than the original FNLMS algorithm. The proposed algorithm is denoted Partial Update-FNLMS (PU-FNLMS) algorithm. Experiments are carried out using stationary and non-stationary inputs filtered by an acoustic car impulse response. Simulation results demonstrated the good performances of the proposed algorithm compared to the NLMS algorithm, and this proposed algorithm has the same performance in the transient and permanent phase as the Full-Update FNLMS algorithm but with a lower arithmetic cost. © 2022 ieee.

关键词： Adaptive filtering

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