Image Steganography Using Deep Learning Techniques
Digital image steganography is the process of embedding information withina cover image in a secure, imperceptible, and recoverable way.The three main methods of digital image steganography are spatial, transform, and neural network methods. Spatial methods modify the pixel valuesof an image to embed information, while transform methods embed hidden information within the frequency of the image.Neural network-based methods use neural networks to perform the hiding process, which is the focus of the proposed methodology.
This research explores the use of deep convolutional neural networks (CNNs) in digital image steganography. This work extends an existing implementation that used a two-dimensional CNN to perform the preparation, hiding, and extraction phases of the steganography process. The methodology proposed in this research, however, introduced changes into the structure of the CNN and used a gain function based on several image similarity metrics to maximize the imperceptibility between a cover and steganographic image.
The performance of the proposed method was measuredusing some frequently utilized image metrics such as structured similarity index measurement (SSIM), mean square error (MSE), and peak signal to noise ratio (PSNR). The results showed that the steganographic images produced by the proposed methodology areimperceptible to the human eye, while still providing good recoverability. Comparingthe results of the proposed methodologyto theresults of theoriginalmethodologyrevealed that our proposed network greatly improved over the base methodology in terms of SSIM andcompareswell to existing steganography methods.
College of Engineering, Technology, and Computer Science at Purdue University Fort Wayne
- Master of Science
- Computer Science
- Fort Wayne