Volume: 1 Issue: 2
Year: 2024, Page: 55-59, Doi: https://doi.org/10.54839/ijeaca.v1i2.2
Received: Sept. 16, 2024 Accepted: Nov. 22, 2024 Published: Dec. 11, 2024
Digital image steganography, the concealing of information within seemingly innocent photographs presents considerable hurdles for traditional detection methods. To address this, we provide a unique deep learning-based steganalysis model is designed to identify the hidden data in digital photos. The methods used to detect steganographic content accurately against a variety of steganographic strategies using convolutional neural networks (CNNs) and generative adversarial networks (GANs). Our model has a multi-stage architecture that includes modules for feature extraction, representation learning, and decision-making, all of which are meant to capture complicated patterns indicating steganographic modifications. We use cutting-edge CNN architectures like ResNet and DenseNet for feature extraction, allowing the model to detect tiny visual cues typical of steganographic embedding. Furthermore, to improve the model's capacity to generalize across diverse steganographic procedures and payloads, we incorporate GAN-based data augmentation approaches, allowing it to learn a more thorough representation of steganographic content variants. Experimental results show that our methodology is effective at recognizing steganographic content with high precision and recall rates, beating existing methods across a variety of parameters. Furthermore, we undertake extensive experiments to evaluate the model's resilience to adversarial attacks and capacity to extend to previously unknown steganographic techniques, confirming its robustness and practical usefulness in real-world contexts.
Keywords: Feature Extraction Approaches for Image Steganalysis: A Review
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© 2024 Patil & Suryawanshi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Patil JJ, Suryawanshi NA. (2024). Feature Extraction Approaches for Image Steganalysis: A Review. International Journal of Electronics and Computer Applications. 1(2): 55-59. https://doi.org/10.54839/ijeaca.v1i2.2