Results 31 to 40 of about 6,282 (220)
Quantized Gaussian JPEG Steganography and Pool Steganalysis
IEEE Access, 2022 Currently, algorithms for compressed image steganography mainly embed hidden message by minimizing the resulting distortion or statistical detectability.
Mohammed Aloraini +2 more
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Steganalysis in resized images [PDF]
2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013 It is well known that the security of a given steganographic algorithm strongly depends on the statistical properties of the cover source. In this paper, we study how downsampling affects steganographic security. The secure payload no longer scales according to the square-root law because resizing changes the statistical properties of the cover source.
Jan Kodovský, Jessica J. Fridrich
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Steganalysis Bukti Digital pada Media Storage Menggunakan Metode GCFIM
JISKA (Jurnal Informatika Sunan Kalijaga), 2020 Steganography is an anti-forensic technique that allows a criminal to hide information in other messages, so that during an examination it will be difficult to obtain evidence of the crime information.
Muh Hajar Akbar +2 more
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Steganalysis of Adaptive Multi-Rate Speech Using Statistical Characteristics of Pitch Delay [PDF]
Journal of Universal Computer Science, 2019 Steganography is a promising technique for covert communications. However, illegal United States of Americage of this technique would facilitate cybercrime activities and thereby pose a great threat to information security.
Hui Tian +5 more
doaj +3 more sources
Deep Residual Network for Steganalysis of Digital Images
IEEE Transactions on Information Forensics and Security, 2019 Steganography detectors built as deep convolutional neural networks have firmly established themselves as superior to the previous detection paradigm – classifiers based on rich media models.
M. Boroumand, Mo Chen, J. Fridrich
semanticscholar +1 more source
Digital Image Steganalysis Based on Visual Attention and Deep Reinforcement Learning
IEEE Access, 2019 Recently, the adaptive steganography methods have been developed to embed secret information with the minimal distortion of images. As the opposite art, steganalysis methods, especially some convolutional neural network-based steganalysis methods, have ...
Donghui Hu +5 more
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Consensus-Clustering-Based Automatic Distribution Matching for Cross-Domain Image Steganalysis
IEEE Transactions on Knowledge and Data Engineering, 2023 Image steganalysis is a technique to detect whether an image contains hidden information. Although the existing cross-domain steganalysis methods have been presented to narrow the distribution gap between different domains, it is still challenging to ...
Ju Jia +4 more
semanticscholar +1 more source
Sensitivity of deep learning applied to spatial image steganalysis [PDF]
PeerJ Computer Science, 2021 In recent years, the traditional approach to spatial image steganalysis has shifted to deep learning (DL) techniques, which have improved the detection accuracy while combining feature extraction and classification in a single model, usually a ...
Reinel Tabares-Soto +8 more
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About JPEG Images Parameters Impact to Steganalys Accuracy
Кібернетика та комп'ютерні технології, 2021 Introduction. Existing examples of illegal use of computer steganography prove the need for the development of stegananalytical methods and systems as one of the most important areas of cybersecurity.
N. Koshkina
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Continual Learning for Steganalysis
CoRR, 2022 To detect the existing steganographic algorithms, recent steganalysis methods usually train a Convolutional Neural Network (CNN) model on the dataset consisting of corresponding paired cover/stego-images. However, it is inefficient and impractical for those steganalysis tools to completely retrain the CNN model to make it effective against both the ...
Zihao Yin, Ruohan Meng, Zhili Zhou 0001
openaire +2 more sources