A THREE-COMPONENT FEATURE EXTRACTION USING DDS_SE-NET FOR EFFICIENT DEEP LEARNING BASED IMAGE STEGANALYSIS FOR REAL WORLD IMAGES

Abstract

The proposed invention, DDS_SE-Net, introduces a novel image steganalysis system designed for detecting steganographic content in real-world datasets. Integrating Dilated Convolutions, Depthwise Separable Convolutions, and Squeeze-and-Excitation (SE) blocks, the system achieves high accuracy (over 92%) against advanced algorithms like WOW, S-UNIWARD, and HILL. By leveraging multi-scale feature detection, adaptive channel weighting, and efficient processing, DDS_SE-Net reduces computational costs and overfitting while maintaining robustness across diverse scenarios. Its modular and scalable design supports real-time applications and seamless integration into cybersecurity frameworks. This invention bridges the gap between theoretical research and practical deployment, paving the way for enhanced digital security solutions.

Specification

Description:The proposed system, "DDS_SE-Net," pertains to the field of image steganalysis within the domain of cybersecurity and artificial intelligence. It focuses on developing an advanced architecture for detecting steganographic content embedded in digital images, ensuring enhanced network and information security. Utilizing a unique combination of Dilated Convolutions, Depthwise Separable Convolutions, and Squeeze-and-Excitation (SE) blocks, the system addresses challenges like overfitting, high computational costs, and loss of coverage in feature extraction. By leveraging real-world datasets and innovative techniques, this system offers improved accuracy and computational efficiency, making it suitable for detecting covert communication channels in images, which could be exploited for illicit purposes. The invention supports secure communication monitoring, enhancing the ability to safeguard against threats to social stability and national security.
Background of the proposed invention:
The proposed invention, "DD , Claims:1. The system employs a novel three-component feature extraction technique using Dilated Convolutions, Depthwise Separable Convolutions, and Squeeze-and-Excitation (SE) blocks for enhanced image steganalysis accuracy.
2. The invention is tailored to process real-world datasets with varied dimensions, textures, and noise levels, ensuring high accuracy and generalization across diverse images.
3. Claim 1 is further enhanced by incorporating dilated convolutions to expand the receptive field and detect multi-scale features without increasing computational parameters.
4. Claim 2 is refined with Depthwise Separable Convolutions, which reduce the system's memory and computational requirements while maintaining high detection precision.
5. The SE blocks in Claim 1 add adaptive channel weighting, improving sensitivity to steganographic distortions while suppressing irrelevant features.
6. The architecture supports real-time analysis and low-latency operations, leveraging Claim 4 for efficient deployment in resource-c

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