Natural Image Statistics for Digital Image Forensics

We describe a set of natural image statistics that are built upon two multi-scale image decompositions, the quadrature mirror filter pyramid decomposition and the local angular harmonic decomposition. These image statistics consist of first- and higher-order statistics that capture certain statistical regularities of natural images. We propose to apply these image statistics, together with classification techniques, to three problems in digital image forensics: (1) differentiating photographic images from computer-generated photorealistic images, (2) generic steganalysis; (3) rebroadcast image detection. We also apply these image statistics to the traditional art authentication for forgery detection and identification of artists in an art work. For each application we show the effectiveness of these image statistics and analyze their sensitivity and robustness

New steerable pyramid steganography algorithm resistant to the Fisher Linear Discriminant steganalysis

This paper describes a new steganography algorithm based on a steerable pyramid transform of a digital image and the steganalysis of the existence of secret messages hidden by this new method. The data embedding process uses the elements of a Lee and Chen steganography algorithm which is adapted to the steerable pyramid transform domain. This article describes the Fisher Linear Disriminant (FLD) analysis and its steganalysis application, too. The main part of the paper is the description of the conducted research and the results of FLD steganalysis of stegoimages produced by the new steganography algorithm

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

Limits of Reliable Communication with Low Probability of Detection on AWGN Channels

We present a square root limit on the amount of information transmitted reliably and with low probability of detection (LPD) over additive white Gaussian noise (AWGN) channels. Specifically, if the transmitter has AWGN channels to an intended receiver and a warden, both with non-zero noise power, we prove that $o(\sqrt{n})$ bits can be sent from the transmitter to the receiver in $n$ channel uses while lower-bounding $\alpha+\beta\geq1-\epsilon$ for any $\epsilon>0$, where $\alpha$ and $\beta$ respectively denote the warden's probabilities of a false alarm when the sender is not transmitting and a missed detection when the sender is transmitting. Moreover, in most practical scenarios, a lower bound on the noise power on the channel between the transmitter and the warden is known and $O(\sqrt{n})$ bits can be sent in $n$ LPD channel uses. Conversely, attempting to transmit more than $O(\sqrt{n})$ bits either results in detection by the warden with probability one or a non-zero probability of decoding error at the receiver as $n\rightarrow\infty$.Comment: Major revision in v2. Context, esp. the relationship to steganography updated. Also, added discussion on secret key length. Results are unchanged from previous version. Minor revision in v3. Major revision in v4, Clarified derivations (adding appendix), also context, esp. relationship to previous work in communication updated. Results are unchanged from previous revision