Neural Style Transfer for image within images and conditional GANs for destylization

In this paper, the feature representation of an image by CNN is used to hide the secret image into the cover image. The style of the cover image hides the content of the secret image and produce a stego image using Neural Style Transfer (NST) algorithm, which resembles the cover image and also contains the semantic content of secret image. The main technical contributions are to hide the content of the secret image in the in-between hidden layered style features of the cover image, which is the first of its kind in the present state-of-art-technique. Also, to recover the secret image from the stego image, destylization is done with the help of conditional generative adversarial networks (GANs) using Residual in Residual Dense Blocks (RRDBs). Further, stego images from different layer combinations of content and style features are obtained and evaluated. Evaluation is based on the visual similarity and quality loss between the cover-stego pair and the secret-reconstructed secret pair of images. From the experiments, it has been observed that the proposed algorithm has 43.95 dB Peak Signal-to-Noise Ratio (PSNR)), .995 Structural Similarity Index (SSIM), and .993 Visual Information Fidelity (VIF) for the ImageNet dataset. The proposed algorithm is found to be more robust against StegExpose than the traditional methods.     

A novel multi-group exploiting modification direction method based on switch map

In this paper, we proposed a novel adjustable data hiding method. Our proposed method, which is based on exploiting modification direction (EMD) method, is called the “multi-group exploiting modification direction” method. In the multi-group method, we combine several pixel-groups to embed secret data according to the constructed switch map to simply avoid the conversion redundancy of the EMD method and the spatial redundancy of the section-wise strategy, as well as to efficiently increase the probability of achieving larger embedding capacity. According to the experimental results and mathematical justification, we verified that the multi-group method can achieve higher embedding payload and better visual quality of the image than the EMD method and section-wise strategy for simulated and real secret data.     

Quantitation Method to the Imperceptibility of ：-Iiding-Vector in Multi-dimensional Hiding Space

Currently, few general methods have been suggested to quantitate the imperceptibility among var- ious hiding algorithms. Especially, it is a challenge to quantitate the imperceptibility of the hiding-vector, which is consisted of orthogonal hiding algorithms in the multi- dimensional hiding space. In this paper, a novel model of general imperceptibility quantitation was proposed. Ac- cording to this model, a quantitation method based on the relative entropy was designed. It is proved under this quantitation method that there exists the global maximal imperceptibility for any specified hiding-vector capacity. An optimization algorithm was proposed to approach this maximum by adjusting the allocation of secret message into each components of the hiding-vector. Experiments with VoIP and bitmap multi-dimensional hiding space validated the effectiveness of the designed quantitation method and the proposed optimization algorithm.     

基于多种检测像素预测方式的LSB图像信息隐藏检测

本文突破了传统LSB隐写分析算法只针对图片整体特征提取、监测手段不够精细的缺陷,提出了针对每一像素点的针对性判断。算法主要利用图像函数估计、噪声判断、像素值估计等方法,通过预测每一像素是否被改动,来判断整张图片的嵌入情况,从而实现小嵌入率隐写分析与嵌入点大致估计的目标。算法的检测率较以往算法有明显提高,对低嵌入率（1%以内）的LSB嵌入检测效果尤为有效。同时对高嵌入率的嵌入,算法保证了99.9%以上的准确率。     

Hiding data in compressive sensed measurements: A conditionally reversible data hiding scheme for compressively sensed measurements

Most of the real-world signals we encounter in real-life applications have low information content. In other words, these signals can be well approximated by sparse signals in a proper basis. Compressive sensing framework uses this fact and attempts to represent signals by using far fewer measurements as compared to conventional acquisition systems. While the CS acquisition is linear, the reconstruction of the signal from its sparse samples is nonlinear and complex. The sparse nature of the signal allows enough room for some additional data sequence to be inserted and exactly recovered along with the reconstructed signal. In this study, we propose to linearly embed and hide data in compressively sensed signals and nonlinearly reconstruct both of them using a deflationary approach. We investigate the embedding capacity as a function of signal sparsity and signal compression, as well as the noise sensitivity of the proposed algorithm.     

An active steganalysis approach for echo hiding based on Sliding Windowed Cepstrum

This paper presents an active steganalysis technique for echo hiding (EH). This approach can differentiate stego audios (with hidden message) from nature ones (without hidden message), and then extract sequential EH messages in stego audios without prior knowledge about the EH technique. The procedure of this algorithm can be simplified as follows: (i) Sliding Windowed Cepstrum (SWC) is obtained by employing a Sliding Window (SW) to move over the audio signal sample by sample. (ii) The echo detector is designed based on statistical analysis of the Cepstrum Peak Location Aggregation Degree (CPLAD). (iii) For stego signals, both echo delay and the range of segment length are estimated by exploring the changing rule of the SWC. (iv) The accurate estimation of segment length and synchronism position is acquired by the grid search algorithm. (v) Finally, the binary secret message can be extracted using the estimated parameters in the previous steps. The proposed method has been implemented and tested to extract the message which is embedded into audio clips by using EH techniques with different kernels. The experimental results show that the accuracy of extraction is no less than 90% when attenuation is greater than 0.45.     

A novel image steganography scheme with high embedding capacity and tunable visual image quality based on a genetic algorithm

Steganography is knowledge and art of hiding secret data into information which is largely used in information security systems. Various methods have been proposed in the literature which most of them are not capable of both preventing visual degradation and providing a large embedding capacity. In this paper, we propose a tunable visual image quality and data lossless method in spatial domain based on a genetic algorithm (GA). The main idea of the proposed technique is modeling the steganography problem as a search and optimization problem. Experimental results, in comparison with other currently popular steganography techniques, demonstrate that the proposed algorithm not only achieves high embedding capacity but also enhances the PSNR of the stego image.     

A novel data hiding scheme based on modulus function

Four criteria are generally used to evaluate the performance of data hiding scheme: the embedding capacity, the visual quality of the stego-image, the security, and the complexity of the data-embedding algorithm. However, data hiding schemes seldom take all these factors into consideration. This paper proposes a novel data hiding scheme that uses a simple modulus function to address all the performance criteria listed above. According to the input secret keys, the encoder and decoder use the same set-generation functions H_r() and H_c() to first generate two sets Kr and Kc. A variant Cartesian product is then created using Kr and Kc. Each cover pixel then forms a pixel group with its neighboring pixels by exploiting an efficient modulus function; the secret data are then embedded or extracted via a mapping process between the variant of the Cartesian product and each pixel group. The proposed scheme offers several advantages, namely (1) the embedding capacity can be scaled, (2) a good visual quality of the stego-image can be achieved, (3) the computational cost of embedding or extracting the secret data is low and requires little memory space, (4) secret keys are used to protect the secret data and (5) the problem of overflow or underflow does not occur, regardless of the nature of the cover pixels.We tested the performance of the proposed scheme by comparing it with Mielikainen’s and Zhang and Wang’s schemes for gray-scale images. The experimental results showed that our proposed scheme outperforms Mielikainen’s in three respects, namely scalable embedding capacity, embedding rate, and level of security. Our data hiding scheme also achieved a higher embedding capacity than Zhang and Wang’s. The proposed scheme can easily be applied to both gray-scale and color images. Analyses of its performance showed that our proposed scheme outperforms Tsai and Wang’s in terms of its time complexity and memory space requirement.     

Reversible steganographic method using SMVQ approach based on declustering

Reversible steganography allows an original image to be completely restored after the extraction of hidden data embedded in a cover image. In this paper, we propose a reversible scheme for VQ-compressed images that is based on a declustering strategy and takes advantage of the local spatial characteristics of the image. The main advantages of our method are ease of implementation, low computational demands, and no requirement for auxiliary data. The experimental results show that the embedding capacity of the proposed method is controlled by the number of declustered groups and the spatial characteristics of the cover image. The results also show that the proposed method has the best embedding capacity and the least running time compared with other methods.