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- Title
Enhanced Steganalysis for Color Images Using Curvelet Features and Support VectorMachine.
- Authors
Akram, Arslan; Khan, Imran; Rashid, Javed; Saddique, Mubbashar; Idrees, Muhammad; Ghadi, Yazeed Yasin; Algarn, Abdulmohsen
- Abstract
Algorithms for steganography are methods of hiding data transfers in media files. Several machine learning architectures have been presented recently to improve stego image identification performance by using spatial information, and these methods have made it feasible to handle a wide range of problems associated with image analysis. Images with little information or low payload are used by information embedding methods, but the goal of all contemporary research is to employ high-payload images for classification. To address the need for both low- and high-payload images, this work provides a machine-learning approach to steganography image classification that uses Curvelet transformation to efficiently extract characteristics from both type of images. SupportVectorMachine (SVM), a commonplace classification technique, has been employed to determinewhether the image is a stego or cover. TheWavelet ObtainedWeights (WOW), Spatial UniversalWavelet Relative Distortion (S-UNIWARD), Highly Undetectable Steganography (HUGO), and Minimizing the Power of Optimal Detector (MiPOD) steganography techniques are used in a variety of experimental scenarios to evaluate the performance of the proposedmethod. UsingWOWat several payloads, the proposed approach proves its classification accuracy of 98.60%. It exhibits its superiority over SOTA methods.
- Subjects
IMAGE recognition (Computer vision); COLOR image processing; IMAGE analysis; MACHINE learning; CRYPTOGRAPHY
- Publication
Computers, Materials & Continua, 2024, Vol 78, Issue 1, p1311
- ISSN
1546-2218
- Publication type
Article
- DOI
10.32604/cmc.2023.040512