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Towards Analysis-friendly Face Representation with Scalable Feature and Texture Compression [article]

Shurun Wang, Shiqi Wang, Wenhan Yang, Xinfeng Zhang, Shanshe Wang, Siwei Ma, Wen Gao
2020 arXiv   pre-print
In particular, we study the feature and texture compression in a scalable coding framework, where the base layer serves as the deep learning feature and enhancement layer targets to perfectly reconstruct  ...  Based on the strong generative capability of deep neural networks, the gap between the base feature layer and enhancement layer is further filled with the feature level texture reconstruction, aiming to  ...  analysis and understanding, especially in real-time application circumstances such as smart city and Internet of Video Things (IoVT) [5] .  ... 
arXiv:2004.10043v1 fatcat:ijgozsonkjbtjd6q6cjgxuugc4

DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images

Honggang Chen, Xiaohai He, Linbo Qing, Shuhua Xiong, Truong Q. Nguyen
2018 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
JPEG is one of the widely used lossy compression methods. JPEG-compressed images usually suffer from compression artifacts including blocking and blurring, especially at low bit-rates.  ...  Inspired by the excellent performance of the deep convolutional neural networks (CNNs) on both low-level and high-level computer vision problems, we develop a dual pixel-wavelet domain deep CNNs-based  ...  Existing methods for soft decoding of JPEG-compressed images can be roughly split into three categories: enhancement-based, restoration-based, and learning-based methods.  ... 
doi:10.1109/cvprw.2018.00114 dblp:conf/cvpr/ChenHQXN18 fatcat:rllwqhn2hbdmzckqh5biq7qyxm

DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images [article]

Honggang Chen and Xiaohai He and Linbo Qing and Shuhua Xiong and Truong Q. Nguyen
2018 arXiv   pre-print
JPEG is one of the widely used lossy compression methods. JPEG-compressed images usually suffer from compression artifacts including blocking and blurring, especially at low bit-rates.  ...  Inspired by the excellent performance of the deep convolutional neural networks (CNNs) on both low-level and high-level computer vision problems, we develop a dual pixel-wavelet domain deep CNNs-based  ...  Existing methods for soft decoding of JPEG-compressed images can be roughly split into three categories: enhancement-based, restoration-based, and learning-based methods.  ... 
arXiv:1805.10558v1 fatcat:sitqnybqmbeexpr36yuoe2yyua

A Unified Framework for Encryption and Decryption of Images Based on Autoencoder (UFED)

2021 International Journal of Advanced Trends in Computer Science and Engineering  
We achieved the best image-compression ratio with Autoencoder over JPEG; JPEG typically achieves 10:1 compression with little perceptible loss in image quality.  ...  However, video coding based on deep training is still in its initial stage. This research work discusses the representative's work on deep learning for image/video coding, a research area since 2015.  ...  The Internet of Things (IoT) has undeniable influence (IoT), IoT devices are now in 60 percent of homes in developing countries that have access to the internet.  ... 
doi:10.30534/ijatcse/2021/451032021 fatcat:6ykv4j3bnnbgzbub7uop46r7gi

Variable-Rate Deep Image Compression With Vision Transformers

Binglin Li, Jie Liang, Jingning Han
2022 IEEE Access  
We show that our patch-based learned image compression with transformers obtain 0.75dB improvement in PSNR at 0.15bpp than the prior variable-rate compression work on the Kodak dataset.  ...  We propose a patch-based learned image compression network by incorporating vision transformers.  ...  In [17] , a discrete wavelet transform based DL model is proposed for internet of underwater things. [18] presents a compression model using the convolutional neural network for remote sensing images  ... 
doi:10.1109/access.2022.3173256 fatcat:ctzts747mrh4flxkzrbjnkpomq

Machine Learning Techniques for Image Forensics in Adversarial Setting

Ehsan Nowroozi, Mauro Barni, Benedetta Tondi
2020 Zenodo  
By exploiting deep learning tools, new approaches have been proposed whose performance remarkably exceeds those achieved by state-of-the-art methods based on standard machine-learning and model-based techniques  ...  The analysis of the security of machine learning-based techniques in the presence of an adversary attempting to impede the forensic analysis, and the development of new solutions capable to improve the  ...  Besides, to my sisters, Nazanin, Negin and Noshin Nowroozi, and my brother Ebrahim Nowroozi, always encouraged me and supported me in all aspects during my studies.  ... 
doi:10.5281/zenodo.4559666 fatcat:itun24lqq5blxfrvmg67pa7ddq

Double JPEG compression forensics based on a convolutional neural network

Qing Wang, Rong Zhang
2016 EURASIP Journal on Information Security  
Experimental results show that the proposed algorithm performs well in double JPEG compression detection and forgery localization, especially when the first compression quality factor is higher than the  ...  To address this challenge, this paper proposes a double JPEG compression detection algorithm based on a convolutional neural network (CNN).  ...  Acknowledgements This work is supported by the National Nature Science Foundation of China under Grant No.61331020.  ... 
doi:10.1186/s13635-016-0047-y fatcat:k3tkxto23zbhfa6p2h2glddnga

Recasting Residual-based Local Descriptors as Convolutional Neural Networks: an Application to Image Forgery Detection [article]

Davide Cozzolino, Giovanni Poggi, Luisa Verdoliva
2017 arXiv   pre-print
Nonetheless, motivated by promising results in computer vision, the focus of the research community is now shifting on deep learning.  ...  In this paper we show that a class of residual-based descriptors can be actually regarded as a simple constrained convolutional neural network (CNN).  ...  The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the  ... 
arXiv:1703.04615v1 fatcat:nh66gsd5fjbm3bguwsplihx2v4

2019 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 29

2019 IEEE transactions on circuits and systems for video technology (Print)  
Kang, X., +, TCSVT July 2019 1919-1932 Energy-Aware Encryption for Securing Video Transmission in Internet of Multimedia Things.  ...  ., Energy-Aware Encryp- tion for Securing Video Transmission in Internet of Multimedia Things; TCSVT March 2019 610-624 Thomas, S.S., Gupta, S., and Subramanian, V.K., Context Driven Optimized Perceptual  ... 
doi:10.1109/tcsvt.2019.2959179 fatcat:2bdmsygnonfjnmnvmb72c63tja

Stegomalware: A Systematic Survey of MalwareHiding and Detection in Images, Machine LearningModels and Research Challenges [article]

Rajasekhar Chaganti, Vinayakumar Ravi, Mamoun Alazab, Tuan D. Pham
2021 arXiv   pre-print
the Deep Learning(DL) models for hiding data detection.  ...  Malware distribution to the victim network is commonly performed through file attachments in phishing email or from the internet, when the victim interacts with the source of infection.  ...  ACKNOWLEDGMENT The authors would like to  ... 
arXiv:2110.02504v1 fatcat:wz5hnqeixrcdjc4afoyycjp7ui

A survey of machine learning techniques in adversarial image forensics

Ehsan Nowroozi, Ali Dehghantanha, Reza M. Parizi, Kim-Kwang Raymond Choo
2020 Zenodo  
Therefore, with a focus on image forensics, this paper surveys techniques that can be used to enhance the robustness of machine learning-based binary manipulation detectors in various adversarial scenarios  ...  Increasingly, machine learning approaches are also utilized in image forensics.  ...  Acknowledgements The first author thanks members of the Visual Information Processing and Protection (VIPP) group at the University of Siena, Italy for their suggestions.  ... 
doi:10.5281/zenodo.4560205 fatcat:zuplnvtwhzhbnajteyunddphkq

Preliminary Forensics Analysis of DeepFake Images [article]

Luca Guarnera
2020 arXiv   pre-print
One of the most terrifying phenomenon nowadays is the DeepFake: the possibility to automatically replace a person's face in images and videos by exploiting algorithms based on deep learning.  ...  To solve this, a preliminary idea on how to fight DeepFake images of faces will be presented by analysing anomalies in the frequency domain.  ...  In [21] , Shen et al. a novel method based on residual image learning for face attribute manipulation is proposed.  ... 
arXiv:2004.12626v5 fatcat:r4ngy6vi5nbnzkionkaib34tw4

ICASSP 2020 Table of Contents

2020 ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
DETECTION IN JPEG IMAGES OBTAINED ..............................................  ...  , Zhan Ma, Nanjing University, China IVMSP-P2.3: BINARY PROBABILITY MODEL FOR LEARNING BASED IMAGE ..................................................... 2168 COMPRESSION Théo Ladune, Pierrick Philippe,  ...  OF THINGS IOT-P1.1 : INFORMATION FLOW OPTIMIZATION IN INFERENCE NETWORKS .................................................  ... 
doi:10.1109/icassp40776.2020.9054406 fatcat:6h7hh2hxhne4pbmphharu2et2m

MFAN: Multi-Level Features Attention Network for Fake Certificate Image Detection

Yu Sun, Rongrong Ni, Yao Zhao
2022 Entropy  
In order to extract features with rich scale information in the encoder, on the one hand, we employ Atrous Spatial Pyramid Pooling (ASPP) on the final layer of a pre-trained residual network to capture  ...  To expand the application of image forensics technology, forgery detection for certificate images that can directly represent people's rights and interests is investigated in this paper.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/e24010118 pmid:35052144 pmcid:PMC8774785 fatcat:ud7y3632rne47idcenwdohi564

Collaborative Intelligence: Challenges and Opportunities [article]

Ivan V. Bajić, Weisi Lin, Yonghong Tian
2021 arXiv   pre-print
The paper surveys the current state of the art in CI, with special emphasis on signal processing-related challenges in feature compression, error resilience, privacy, and system-level design.  ...  Our goal is to raise awareness in the signal processing community of the challenges and opportunities in this area of growing importance, where key developments are expected to come from signal processing  ...  The authors in [12] introduced feature and texture compression in a scalable coding framework, where the base layer is catered for the deep learning feature while the enhancement layer aims to reconstruct  ... 
arXiv:2102.06841v1 fatcat:344kpjhvrbabxbmb6rzlrgxlo4
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