High capacity steganographic method based upon JPEG

The two most important aspects of any image-based steganographic system are the quality of the stegoimage and the capacity of the cover image. This paper proposes a novel and high capacity steganographic approach based on Discrete Cosine Transformation (DCT) and JPEG compression. JPEG technique divides the input image into non-overlapping blocks of 8x8 pixels and uses the DCT transformation. However, our proposed method divides the cover image into nonoverlapping blocks of 16x16 pixels. For each quantized DCT block, the least two-significant bits (2-LSBs) of each middle frequency coefficient are modified to embed two secret bits. Our aim is to investigate the data hiding efficiency using larger blocks for JPEG compression. Our experiment result shows that the proposed approach can provide a higher information hiding capacity than Jpeg-Jsteg and Chang et al. methods based on the conventional blocks of 8x8 pixels. Furthermore, the produced stego-images are almost identical to the original cover images

JPEG steganography: A performance evaluation of quantization tables

The two most important aspects of any image based steganographic system are the imperceptibility and the capacity of the stego image. This paper evaluates the performance and efficiency of using optimized quantization tables instead of default JPEG tables within JPEG steganography. We found that using optimized tables significantly improves the quality of stego-images. Moreover, we used this optimization strategy to generate a 16x16 quantization table to be used instead of that suggested. The quality of stego-images was greatly improved when these optimized tables were used. This led us to suggest a new hybrid steganographic method in order to increase the embedding capacity. This new method is based on both and Jpeg-Jsteg methods. In this method, for each 16x16 quantized DCT block, the least two significant bits (2-LSBs) of each middle frequency coefficient are modified to embed two secret bits. Additionally, the Jpeg-Jsteg embedding technique is used for the low frequency DCT coefficients without modifying the DC coefficient. Our experimental results show that the proposed approach can provide a higher information-hiding capacity than the other methods tested. Furthermore, the quality of the produced stego-images is better than that of other methods which use the default tables

Side-Information For Steganography Design And Detection

Today, the most secure steganographic schemes for digital images embed secret messages while minimizing a distortion function that describes the local complexity of the content. Distortion functions are heuristically designed to predict the modeling error, or in other words, how difficult it would be to detect a single change to the original image in any given area. This dissertation investigates how both the design and detection of such content-adaptive schemes can be improved with the use of side-information. We distinguish two types of side-information, public and private: Public side-information is available to the sender and at least in part also to anybody else who can observe the communication. Content complexity is a typical example of public side-information. While it is commonly used for steganography, it can also be used for detection. In this work, we propose a modification to the rich-model style feature sets in both spatial and JPEG domain to inform such feature sets of the content complexity. Private side-information is available only to the sender. The previous use of private side-information in steganography was very successful but limited to steganography in JPEG images. Also, the constructions were based on heuristic with little theoretical foundations. This work tries to remedy this deficiency by introducing a scheme that generalizes the previous approach to an arbitrary domain. We also put forward a theoretical investigation of how to incorporate side-information based on a model of images. Third, we propose to use a novel type of side-information in the form of multiple exposures for JPEG steganography

A multi-task learning CNN for image steganalysis

Convolutional neural network (CNN) based image steganalysis are increasingly popular because of their superiority in accuracy. The most straightforward way to employ CNN for image steganalysis is to learn a CNN-based classifier to distinguish whether secret messages have been embedded into an image. However, it is difficult to learn such a classifier because of the weak stego signals and the limited useful information. To address this issue, in this paper, a multi-task learning CNN is proposed. In addition to the typical use of CNN, learning a CNN-based classifier for the whole image, our multi-task CNN is learned with an auxiliary task of the pixel binary classification, estimating whether each pixel in an image has been modified due to steganography. To the best of our knowledge, we are the first to employ CNN to perform the pixel-level classification of such type. Experimental results have justified the effectiveness and efficiency of the proposed multi-task learning CNN