Deep Convolutional Generative Adversarial Network for Image Steganography Enhancement
Keywords:
Adversarial Attacks, Convolutional Neural Network, Deep Convolutional Generative Adversarial Network, Discriminator and Image SteganographyAbstract
In recent times, safeguarding data has emerged as a paramount global issue demanding the utmost attention and concern. The secret data is exposed to potential hacks when transmitted via conventional communication channels. The image steganalysis development based on the Convolutional Neural Network (CNN) has become challenging for image steganography. However, the recent steganographic approaches are complex to resist the detection of CNN-based steganalyzers. To address this issue, this research proposed the image steganographic plan based on a Deep Convolutional Generative Adversarial network (DCGAN) with adversarial attack. The proposed method utilized the MNIST steganography dataset to estimate the performance of DCGAN. This is performed to generate the secure DCGAN result, which has greater robustness to adversarial data operations. The experimental results show that the proposed method achieves greater performance and achieves the stego accuracy of 0.9155, discriminator loss of 0.0307 as well as similitude loss of 0.00167 when compared to the existing methods like GAN and Information-driven GAN (IDGAN). The proposed approach can efficiently protect the sensitive data even affecting the quality of image data as well as outperforms the compared existing methods.
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MNIST dataset link: https://www.kaggle.com/code/manthansolanki/image-classification-with-mnist-dataset.
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