Rongrong Ni

Reversible Watermarking Based on Invariability and Adjustment on Pixel Pairs

A novel reversible data hiding scheme based on invariability of the sum of pixel pairs and pairwise difference adjustment (PDA) is presented in this letter. For each pixel pair, if a certain value is added to one pixel while the same value is subtracted from the other, then the sum of these two pixels will remain unchanged. How to properly select this value is the key issue for the balance between reversibility and distortion. In this letter, half the difference of a pixel pair plus 1-bit watermark has been elaborately selected to satisfy this purpose. In addition, PDA is proposed to significantly reduce the capacity consumed by overhead information. A series of experiments is conducted to verify the effectiveness and advantages of the proposed approach.

Pairwise Prediction-Error Expansion for Efficient Reversible Data Hiding

In prediction-error expansion (PEE) based reversible data hiding, better exploiting image redundancy usually leads to a superior performance. However, the correlations among prediction-errors are not considered and utilized in current PEE based methods. Specifically, in PEE, the prediction-errors are modified individually in data embedding. In this paper, to better exploit these correlations, instead of utilizing prediction-errors individually, we propose to consider every two adjacent prediction-errors jointly to generate a sequence consisting of prediction-error pairs. Then, based on the sequence and the resulting 2D prediction-error histogram, a more efficient embedding strategy, namely, pairwise PEE, can be designed to achieve an improved performance. The superiority of our method is verified through extensive experiments.

Contrast Enhancement-Based Forensics in Digital Images

As a retouching manipulation, contrast enhancement is typically used to adjust the global brightness and contrast of digital images. Malicious users may also perform contrast enhancement locally for creating a realistic composite image. As such it is significant to detect contrast enhancement blindly for verifying the originality and authenticity of the digital images. In this paper, we propose two novel algorithms to detect the contrast enhancement involved manipulations in digital images. First, we focus on the detection of global contrast enhancement applied to the previously JPEG-compressed images, which are widespread in real applications. The histogram peak/gap artifacts incurred by the JPEG compression and pixel value mappings are analyzed theoretically, and distinguished by identifying the zero-height gap fingerprints. Second, we propose to identify the composite image created by enforcing contrast adjustment on either one or both source regions. The positions of detected blockwise peak/gap bins are clustered for recognizing the contrast enhancement mappings applied to different source regions. The consistency between regional artifacts is checked for discovering the image forgeries and locating the composition boundary. Extensive experiments have verified the effectiveness and efficacy of the proposed techniques.

Reversible data hiding based on PDE predictor

Abstract In this paper, we propose a prediction-error expansion based reversible data hiding by using a new predictor based on partial differential equation (PDE). For a given pixel, PDE predictor uses the mean of its four nearest neighboring pixels as initial prediction, and then iteratively updates the prediction until the value goes stable. Specifically, for each pixel, by calculating the gradients of four directions, the direction with small magnitude of gradient will be weighted larger in the iteration process, and finally a more accurate prediction can be obtained. Since PDE predictor can better exploit image redundancy, the proposed method introduces less distortion for embedding the same payload. Experimental results show that our method outperforms some state-of-the-art methods.

Forensic detection of median filtering in digital images

In digital image forensics, prior works are prone to the detection of malicious tampering. However, there is also a need for developing techniques to identify general content-preserved manipulations, which are employed to conceal tampering trails frequently. In this paper, we propose a blind forensic algorithm to detect median filtering (MF), which is applied extensively for signal denoising and digital image enhancement. The probability of zero values on the first order difference map in texture regions can serve as MF statistical fingerprint, which distinguishes MF from other operations. Since anti-forensic techniques enjoy utilizing MF to attack the linearity assumption of existing forensics algorithms, blind detection of the non-linear MF becomes especially significant. Both theoretically reasoning and experimental results verify the effectiveness of our proposed MF forensics scheme.

Reversible data hiding using invariant pixel-value-ordering and prediction-error expansion

Recently, Li et al. proposed a reversible data hiding (RDH) method based on pixel-value-ordering (PVO) and prediction-error expansion. In their method, the maximum and the minimum of a pixel block are predicted and modified to embed data, and the reversibility is guaranteed by keeping PVO of each block invariant after embedding. In this paper, a novel RDH method is proposed by extending Li et al.@?s work. Instead of considering only a single pixel with maximum (or minimum) value of a block, all maximum-valued (or minimum-valued) pixels are taken as a unit to embed data. Specifically, the maximum-valued (or minimum-valued) pixels are first predicted and then modified together such that they are either unchanged or increased by 1 (or decreased by 1) in value at the same time. Comparing our method with Li et al.@?s, more blocks suitable for RDH are utilized and image redundancy is better exploited. Moreover, a mechanism of advisable payload partition and pixel-block-selection is adopted to optimize the embedding performance in terms of capacity-distortion behavior. Experimental results verify that our method outperforms Li et al.@?s and some other state-of-the-art works.

Anti-forensics of contrast enhancement in digital images

The blind detection of contrast enhancement in digital images has attracted much attention of the forensic analyzers. In this paper, we propose new variants of contrast enhancement operators which are undetectable by the existing contrast enhancement detectors based on the peak-gap artifacts of the pixel graylevel histogram. Local random dithering is introduced into the design of contrast enhancement mapping for removing such artifacts. Effectiveness of the proposed anti-forensic scheme is validated by experimental results on a large image database for various parameter settings. Both detectability and the resulting image quality are evaluated via comparison with the traditional contrast enhancement. The developed anti-forensic techniques could verify the reliability of existing contrast enhancement forensic tools against sophisticated attackers and serve as the targets for developing more reliable and secure forensic techniques.

Improved adaptive LSB steganography based on chaos and genetic algorithm

We propose a novel steganographic method in JPEG images with high performance. Firstly, we propose improved adaptive LSB steganography, which can achieve high capacity while preserving the first-order statistics. Secondly, in order to minimize visual degradation of the stego image, we shuffle bits-order of the message based on chaos whose parameters are selected by the genetic algorithm. Shuffling messages bits-order provides us with a new way to improve the performance of steganography. Experimental results show that our method outperforms classical steganographic methods in image quality, while preserving characteristics of histogram and providing high capacity.

Forensic estimation of gamma correction in digital images

In the digital era, digital photographs become pervasive and are frequently used to record event facts. Authenticity and integrity of such photos can be ascertained by discovering more information about the previously applied operations. In this paper, we propose a forensic scheme for identifying and reconstructing gamma correction operations in digital images. Statistical abnormity on image grayscale histograms, which is caused by the contrast enhancement, is analyzed theoretically and measured effectively. Graylevel mapping functions involved in gamma correction can be estimated blindly. Experiments both on globally and locally applied corrected images show the validity of our proposed gamma estimation algorithm.

A Novel Reversible Watermarking Based on an Integer Transform

A novel reversible data hiding scheme based on an integer transform is presented in this paper. The invertible integer transform exploits the correlations among four pixels in a quad. Data embedding is carried out by expanding the differences between one pixel and each of its three neighboring pixels. However, the high hiding capacity can not be achieved only by difference expansion, so the companding technique is introduced into the embedding process so as to further increase hiding capacity. A series of experiments are conducted to verify the feasibility and effectiveness of the proposed approach.