Steganography: a class of secure and robust algorithms

This research work presents a new class of non-blind information hiding algorithms that are stego-secure and robust. They are based on some finite domains iterations having the Devaney's topological chaos property. Thanks to a complete formalization of the approach we prove security against watermark-only attacks of a large class of steganographic algorithms. Finally a complete study of robustness is given in frequency DWT and DCT domains.Comment: Published in The Computer Journal special issue about steganograph

Hide Secret Information in Blocks: Minimum Distortion Embedding

In this paper, a new steganographic method is presented that provides minimum distortion in the stego image. The proposed encoding algorithm focuses on DCT rounding error and optimizes that in a way to reduce distortion in the stego image, and the proposed algorithm produces less distortion than existing methods (e.g., F5 algorithm). The proposed method is based on DCT rounding error which helps to lower distortion and higher embedding capacity.Comment: This paper is accepted for publication in IEEE SPIN 2020 conferenc

Pseudo-random number generators and an improved steganographic algorithm

Steganography is the art and science of hiding secret information in a cover medium such that the presence of the hidden information cannot be detected. This thesis proposes a new method of steganography by cover modification in JPEG images. Essentially, the algorithm exercises LSB replacement using the definition for steganographic values from F5. After the nonzero quantized DCT coefficients of a cover image undergo a pseudorandom walk, the coefficients and the payload are split into an equal number of partitions and paired. Each coefficient partition is permuted again by the 1/P pseudo-random number generator until an optimal embedding efficiency for its corresponding payload is achieved. Using this method, we achieve a higher embedding efficiency than that of LSB replacement alone. We evaluate the detectability of our algorithm by creating a multi-classifier based on the output of multiple non-linear, soft-margin support vector machines trained on POMM features. We show that our algorithm performs nearly as well as the state-of-the-art nsF5 algorithm, and outperforms other state-of-the-art algorithms under most conditions

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 COMPARATIVE STUDY OF OBJECT ORIENTED STEGANOGRAPHIC TECHNIQUES

Steganography is defined as camoufling, secret information within other information i.e. hiding information. The steganography’s main objective is to communicate securely in such a manner that the true information/message is not visible to the intruder. Any unwanted parties should not be able to correlate any sense between cover image and stego image. Thus the stego image must be same as the original cover image. In this paper, a comparative study of steganographic methods that use skin tone detection is done. For comparison three methods are considered. At first steganography using DWT is discussed. It is done in frequency domain as we obtain more precise stego images. Here Haar transform is used which leads to four sub bands. The secret data is embedded into one of the high frequency sub band. In the second method, secret data is embedded within skin region of image that provides an excellent secure location for data hiding. Skin tone detection is performed using HSV and YCbCr color space models. The last implementation is performed by applying skin tone detection using YCbCr color space and the edge of those skin pixels is detected using canny edge detection filter and then the secret image is steganoflaged into cover image. Performances of the three techniques are compared based on the PSNR obtained