Cryptospace Invertible Steganography with Conditional Generative Adversarial Networks

Abstract

Deep neural networks have become the foundation of many modern intelligent systems. Recently, the author has explored adversarial learning for invertible steganography (ALIS) and demonstrated the potential of deep neural networks to reinvigorate an obsolete invertible steganographic method. With the worldwide popularisation of the Internet of things and cloud computing, invertible steganography can be recognised as a favourable way of facilitating data management and authentication due to the ability to embed information without causing permanent distortion. In light of growing concerns over cybersecurity, it is important to take a step forwards to investigate invertible steganography for encrypted data. Indeed, the multidisciplinary research in invertible steganography and cryptospace computing has received considerable attention. In this paper, we extend previous work and address the problem of cryptospace invertible steganography with deep neural networks. Specifically, we revisit a seminal work on cryptospace invertible steganography in which the problem of message decoding and image recovery is viewed as a type of binary classification. We formulate a general expression encompassing spatial, spectral, and structural analyses towards this particular classification problem and propose a novel discrimination function based on a recurrent conditional generative adversarial network (RCGAN) which predicts bit-planes with stacked neural networks in a top-down manner. Experimental results evaluate the performance of various discrimination functions and validate the superiority of neural-network-aided discrimination function in terms of classification accuracy.

High-Capacity Reversible Data Hiding in Encrypted Images Based on Prediction Error Compression and Block Selection

“…Later, Hong et al [24] utilized a side match smoothness algorithm to reduce the error rate. Deep learning technique is also introduced to improve the smoothness algorithm [25]. In these schemes, the processes of image recovery and data extraction are joined.…”

Privacy-Preserving Reversible Data Hiding for Medical Images Employing Local Rotation

“…There are six 512 × 512 medical images, i.e., “Kindey_A,” “Kindey_B,” “Brain_A,” “Brain_B,” “Sketeton_A,” and “Sketeton_B,” used as test images, and they are shown in Figure 4 . In our experiments, several statistical metrics, such as PSNR (Peak signal-to-noise ratio) [ 31 ] and EC, are measured for the performance evaluation.…”

A Watermarking Scheme for Color Image Using Quaternion Discrete Fourier Transform and Tensor Decomposition

“…However, the paper divides RGB channels into 8 × 8 × 3 blocks, which cannot resist a cropping attack. Image processing [40,41] could affect the accuracy of watermark extraction.…”