Results 81 to 90 of about 6,801,187 (249)
Neural Steganalysis with Spatial Rich Models for Image Steganography Detection
, 2020 Digital image steganalysis is the process of detecting if an image contains concealed data embedded within its pixel space inserted via a steganography algorithm.
Isaac Corley (8391066) +2 more
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CAAI Transactions on Intelligence Technology, Volume 10, Issue 5, Page 1269-1290, October 2025.Abstract Life expectancy has improved with new‐age technologies and advancements in the healthcare sector. Though artificial intelligence (AI) and the Internet of Things (IoT) are revolutionising smart healthcare systems, security of the healthcare data is always a concern.
Shaiju Panchikkil +4 more
wiley +1 more source
Image Steganalysis via Diverse Filters and Squeeze-and-Excitation Convolutional Neural Network
Mathematics, 2021 Steganalysis is a method to detect whether the objects contain secret messages. With the popularity of deep learning, using convolutional neural networks (CNNs), steganalytic schemes have become the chief method of combating steganography in recent years.
Feng Liu +4 more
semanticscholar +1 more source
CAAI Transactions on Intelligence Technology, Volume 10, Issue 5, Page 1422-1445, October 2025.ABSTRACT Medical images play a crucial role in diagnosis, treatment procedures and overall healthcare. Nevertheless, they also pose substantial risks to patient confidentiality and safety. Safeguarding the confidentiality of patients' data has become an urgent and practical concern.
Mohamed Meselhy Eltoukhy +3 more
wiley +1 more source
Machine Learning in Image Steganalysis
, 2012 Steganography is the art of communicating a secret message, hiding the very existence of a secret message. This is typically done by hiding the message within a non-sensitive document. Steganalysis is the art and science of detecting such hidden messages.
Vrusias, Bogdan, Schaathun, Hans Georg
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Image Steganalysis Based on Deep Content Features Clustering
, 2023 Publisher Copyright: © 2023 Tech Science Press. All rights reserved.The training images with obviously different contents to the detected images will make the steganalysis model perform poorly in deep steganalysis. The existing methods try to reduce this
Zhu, Ma +5 more
core +1 more source
Steganalysis of binary images [PDF]
, 2019 With an increasing use of digital binary images including text, graphic and halftone images in daily life, security of the digital binary images such as certificate, transcript, scanning hard copy to electronic copy etc. is a big concern. Such a concern makes data hiding in binary images an important and attractive research topic in recent years.
openaire +3 more sources
TESOL Quarterly, Volume 59, Issue 2, Page 874-906, June 2025.Abstract The purpose of this study is to analyze the grammatical complexity features of international teaching assistants' (ITAs) mock‐teaching presentations and to compare the distributions of these features to those found in the Oral English Proficiency Test (a local ITA assessment), university classroom teaching, conversation, and academic writing ...
Heesun Chang, Amin Raeisi‐Vanani
wiley +1 more source
Content‐adaptive steganalysis for color images
Security and Communication Networks, 2016 AbstractSome steganography methods for gray‐scale image can be extended to true RGB color image by treating each of its three color channels as a gray‐scale image. In modern popular steganography, most embedding changes are highly concentrated on those complex textural regions with smaller embedding distortions.
Xin Liao 0001 +2 more
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Wavelet‐Based Texture Mining and Enhancement for Face Forgery Detection
IET Biometrics, Volume 2025, Issue 1, 2025.Due to the abuse of deep forgery technology, the research on forgery detection methods has become increasingly urgent. The corresponding relationship between the frequency spectrum information and the spatial clues, which is often neglected by current methods, could be conducive to a more accurate and generalized forgery detection.
Xin Li +4 more
wiley +1 more source