Lifang Yu

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.

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.

PM1 steganography in JPEG images using genetic algorithm

Plus minus 1 (PM1) is an improved method to least significant bits (LSB)-based steganography techniques, which not only foils typical attacks against LSB-based techniques, but also provides high capacity. But how to apply it to JPEG images does not appear in literatures. In this paper, PM1 steganography in JPEG images using genetic algorithm (GA) is proposed, in which the GA is used to optimize the performance, such as minimizing blockiness. Theoretical analysis to the histogram characteristics after steganography is discussed in details, which proves that PM1 used in JPEG images preserves the first-order statistical properties. Experiments show that the proposed method outperforms the other methods in terms of capacity and security.

A high-performance YASS-like scheme using randomized big-blocks

Randomly selecting 8 × 8 host blocks in big-blocks for data embedding, YASS [1], a recently developed advanced stegano-graphic scheme makes these blocks not coincident with the 8×8 grids used in JPEG compression. As a result, it effectively invalidates the self-calibration technique used in modern steganaly-sis. However, the randomization is not sufficient enough, i.e., some positions in an image are possible to hold host blocks and some are definitely not. Based on this observation, the newly developed specific steganalyzer [2] can effectively defeat YASS. In this paper, a new steganographic scheme is presented. Through randomizing the size and position of each big-block, our improved steganographic method makes almost every position possible to hold a host block, which has been verified by our statistical analysis. Consequently, the proposed scheme can survive the attack made by the specific steganalyzer. Experimental results have demonstrated that the detection rate achieved by the specific steganalyzer on our proposed method is less than 58%, while that on YASS is about 95% and above.

Attacking contrast enhancement forensics in digital images

Currently, plenty of digital image forensic techniques have been proposed and used as diagnostic tools. It is urgent and significant to assess the reliability of such techniques applied in practical scenarios. In this paper, we investigate the security of existing digital image contrast enhancement (CE) forensic algorithms. From the standpoint of attackers, we propose two types of attacks, CE trace hiding attack and CE trace forging attack, which could invalidate the forensic detector and fabricate two types of forensic errors, respectively. The CE trace hiding attack is implemented by integrating local random dithering into the design of pixel value mapping. The CE trace forging attack is proposed by modifying the gray level histogram of a target pixel region to counterfeit peak/gap artifacts. Such trace forging attack is typically applied to create sophisticated composite images which could deceive the prior CE-based composition detectors. Extensive experimental results demonstrate the efficacy of our proposed CE anti-forensic schemes.

Fast mutual-information-based contrast enhancement

Recently, T. Celik proposed an effective image contrast enhancement (CE) method based on spatial mutual information and PageRank (SMIRANK). According to the state-of-the-art evaluation criteria, it achieves the best visual enhancement quality among existing global CE methods. However, SMIRANK runs much slower than the other counterparts, such as histogram equalization (HE) and adaptive gamma correction. Low computational complexity is also required for good CE algorithms. In this paper, we novelly propose a fast SMIRANK algorithm, called FastSMIRANK. It integrates both spatial and gray-level downsampling into the generation of pixel value mapping function. Moreover, the computation of rank vectors is speeded up by replacing PageRank with a simple yet efficient row-based operation of mutual information matrix. Extensive experimental results show that the proposed FastSMIRANK could accelerate the processing speed of SMIRANK by about 20 times, and is even faster than HE. Comparable enhancement quality is preserved simultaneously.

Acceleration of Histogram-Based Contrast Enhancement via Selective Downsampling

The authors propose a general framework to accelerate the universal histogram-based image contrast enhancement (CE) algorithms. Both spatial and grey-level selective downsampling of digital images are adopted to decrease computational cost, while the visual quality of enhanced images is still preserved and without apparent degradation. Mapping function calibration is proposed to reconstruct the pixel mapping on the grey levels missed by downsampling. As two case studies, the accelerations of histogram equalisation (HE) and the state-of-the-art global CE algorithm, i.e. spatial mutual information and PageRank (SMIRANK), are presented in detail. Both quantitative and qualitative assessment results have verified the effectiveness of their proposed CE acceleration framework. In typical tests, the computational efficiencies of HE and SMIRANK have been increased by about 3.9 and 13.5 times, respectively.

A channel selection rule for YASS

In this paper, a channel selection rule for YASS (Yet-Another-Secure-Steganography) is proposed. Secret message embedding imposes distortion to the cover image. The larger the distortion, the less secure the steganographic algorithm. Our channel selection rule engages in minimizing this distortion brought in by YASS. In our rule, the distortion caused by unit change on each quantized DCT (Discrete Cosine Transformation) component is computed, and the components with smaller unit change distortion are selected with higher priority. This channel selection rule reduces distortion to the medium spatial domain image and the final JPEG image. Experimental results show that our improved YASS scheme outperforms original YASS scheme on the aspects of both perception and statistics. This new channel selection rule can also be combined with other enhancements in YASS framework to further boost the performance.

Fast contrast enhancement by adaptive pixel value stretching

In this article, we propose a fast and effective method for digital image contrast enhancement. The gray-level dynamic range of contrast-distorted images is extended maximally via adaptive pixel value stretching. The quantity of saturated pixels is set intelligently according to the perceptual brightness of global images. Adaptive gamma correction is also novelly used to recover the normal luminance in enhancing dimmed images. Different from prior methods, our proposed technique could be enforced automatically without complex manual parameter adjustment per image. Both qualitative and quantitative performance evaluation results show that, comparing with some recent influential contrast enhancement techniques, our proposed method achieves comparative or better enhancement quality at a surprisingly lower computational cost. Besides general computer applications, such merit should also be valuable in low-power scenarios, such as the imaging pipelines used in small mobile terminals and visual sensor network.

On YASS's Non-monotonic Security Performance

Recently, researchers have discovered unexpected bumps in the detection rate curve of yet another steganographic scheme (YASS). We refer to this abnormal phenomenon as non-monotonic security performance. This paper first analyzes this abnormality and points out that it is caused by the non-uniformity in probability of coincidence of