Xiao-Chen Yuan

Image Forgery Detection Using Adaptive Oversegmentation and Feature Point Matching

A novel copy-move forgery detection scheme using adaptive oversegmentation and feature point matching is proposed in this paper. The proposed scheme integrates both block-based and keypoint-based forgery detection methods. First, the proposed adaptive oversegmentation algorithm segments the host image into nonoverlapping and irregular blocks adaptively. Then, the feature points are extracted from each block as block features, and the block features are matched with one another to locate the labeled feature points; this procedure can approximately indicate the suspected forgery regions. To detect the forgery regions more accurately, we propose the forgery region extraction algorithm, which replaces the feature points with small superpixels as feature blocks and then merges the neighboring blocks that have similar local color features into the feature blocks to generate the merged regions. Finally, it applies the morphological operation to the merged regions to generate the detected forgery regions. The experimental results indicate that the proposed copy-move forgery detection scheme can achieve much better detection results even under various challenging conditions compared with the existing state-of-the-art copy-move forgery detection methods.

Robust Segments Detector for De-Synchronization Resilient Audio Watermarking

A robust feature points detector for invariant audio watermarking is proposed in this paper. The audio segments centering at the detected feature points are extracted for both watermark embedding and extraction. These feature points are invariant to various attacks and will not be changed much for maintaining high auditory quality. Besides, high robustness and inaudibility can be achieved by embedding the watermark into the approximation coefficients of Stationary Wavelet Transform (SWT) domain, which is shift invariant. The spread spectrum communication technique is adopted to embed the watermark. Experimental results show that the proposed Robust Audio Segments Extractor (RASE) and the watermarking scheme are not only robust against common audio signal processing, such as low-pass filtering, MP3 compression, echo addition, volume change, and normalization; and distortions introduced in Stir-mark benchmark for Audio; but also robust against synchronization geometric distortions simultaneously, such as resample time-scale modification (TSM) with scaling factors up to ±50%, pitch invariant TSM by ±50%, and tempo invariant pitch shifting by ±50%. In general, the proposed scheme can well resist various attacks by the joint RASE and SWT approach, which performs much better comparing with the existing state-of-the art methods.

Fast communication: Geometric invariant watermarking by local Zernike moments of binary image patches

A novel digital image watermarking scheme based on feature extraction and local Zernike transform is proposed in this paper. We proposed a local Zernike moments based watermarking scheme where the watermarked image/region can be obtained directly by inverse Zernike Transform. An edge-based feature detector is proposed for local region extraction, with which, the distinct circular patch of given size can be extracted for watermark embedding and extraction. The extracted circular patch is decomposed into a collection of binary patches and Zernike transform is applied to the selected binary patches. Magnitudes of the local Zernike moments are calculated and modified to embed the watermarks. Experimental results show that the proposed watermarking scheme is very robust against geometric distortion such as rotation, scaling, cropping, and affine transformation; and common signal processing such as JPEG compression, median filtering, and low-pass Gaussian filtering.

Multi-Level Dense Descriptor and Hierarchical Feature Matching for Copy-Move Forgery Detection

In this paper, a Multi-Level Dense Descriptor (MLDD) extraction method and a Hierarchical Feature Matching method are proposed to detect copy-move forgery in digital images. The MLDD extraction method extracts the dense feature descriptors using multiple levels, while the extracted dense descriptor consists of two parts: the Color Texture Descriptor and the Invariant Moment Descriptor. After calculating the MLDD for each pixel, the Hierarchical Feature Matching method subsequently detects forgery regions in the input image. First, the pixels that have similar color textures are grouped together into distinctive neighbor pixel sets. Next, each pixel is matched with pixels in its corresponding neighbor pixel set through its geometric invariant moments. Then, the redundant pixels from previously generated matched pixel pairs are filtered out by the proposed Adaptive Distance and Orientation Based Filtering method. Finally, some morphological operations are applied to generate the final detected forgery regions. Experimental results show that the proposed scheme can achieve much better detection results compared with the existing state-of-the-art CMFD methods, even under various challenging conditions such as geometric transforms, JPEG compression, noise addition and down-sampling.

Multi-scale image hashing using adaptive local feature extraction for robust tampering detection

The main problem addressed in this paper is the robust tampering detection of the image received in a transmission under various content-preserving attacks. To this aim the multi-scale image hashing method is proposed by using the location-context information of the features generated by adaptive and local feature extraction techniques. The generated hash is attached to the image before transmission and analyzed at destination to filter out the geometric transformations occurred in the received image by image restoration firstly. Based on the restored image, the image authentication using the global and color hash component is performed to determine whether the received image has the same contents as the trusted one or has been maliciously tampered, or just different. After regarding the received image as being tampered, the tampered regions will be localized through the multi-scale hash component. Lots of experiments are conducted to indicate that our tampering detection scheme outperforms the existing state-of-the-art methods and is very robust against the content-preserving attacks, including both common signal processing and geometric distortions. Address the problem of Image tamper detection.Novel Multi-scale image hashing using adaptive local feature extraction is proposed.Novel robust tampering detection scheme is proposed.Novel image authentication and tampering localization are proposed.

Digital image forgery detection using JPEG features and local noise discrepancies.

Wide availability of image processing software makes counterfeiting become an easy and low-cost way to distort or conceal facts. Driven by great needs for valid forensic technique, many methods have been proposed to expose such forgeries. In this paper, we proposed an integrated algorithm which was able to detect two commonly used fraud practices: copy-move and splicing forgery in digital picture. To achieve this target, a special descriptor for each block was created combining the feature from JPEG block artificial grid with that from noise estimation. And forehand image quality assessment procedure reconciled these different features by setting proper weights. Experimental results showed that, compared to existing algorithms, our proposed method is effective on detecting both copy-move and splicing forgery regardless of JPEG compression ratio of the input image.

Robust Mel-Frequency Cepstral coefficients feature detection and dual-tree complex wavelet transform for digital audio watermarking

A novel digital audio watermarking scheme based on robust Mel-Frequency Cepstral coefficients feature detection and dual-tree complex wavelet transform is proposed in this paper, which is similar as patchwork based methods that several segments are extracted from the host audio clip for watermarking use. The robust Mel-Frequency Cepstral coefficients feature detection method is proposed to extract the feature segments which should be relocated when the host audio signal attacked by various distortions including both the common audio signal processing and the conventional geometric distortions. With the robust feature segments, the approximate shift invariant transform dual-tree complex wavelet transform based watermarking method is proposed to embed the watermark into the DT CWT real low-pass coefficients of each segment, using the spread spectrum techniques. The linear correlation is calculated to judge the existence of the watermark during the watermark detection. Experimental results show that the proposed digital audio watermarking scheme based on robust Mel-Frequency Cepstral coefficients feature detection and dual-tree complex wavelet transform can achieve high robustness against the common audio signal processing, such as low-pass filtering, MP3 compression, echo addition, volume change, and normalization; and geometric distortions, such as resample Time-Scale Modification (TSM), pitch invariant TSM, and tempo invariant pitch shifting. In addition, the proposed audio watermarking scheme is resilient to Stir-mark for Audio, and it performs much better comparing with the existing state-of-the art methods.

Feature extraction and local Zernike moments based geometric invariant watermarking

A robust and geometric invariant digital image watermarking scheme based on robust feature detector and local Zernike transform is proposed in this paper. The robust feature extraction method is proposed based on the Scale Invariant Feature Transform (SIFT) algorithm, to extract circular regions/patches for watermarking use. Then a local Zernike moments-based watermarking scheme is raised, where the watermarked regions/patches can be obtained directly by inverse Zernike Transform. Each extracted circular patch is decomposed into a collection of binary patches and Zernike transform is applied to the appointed binary patches. Magnitudes of the local Zernike moments are calculated and modified to embed the watermarks. Experimental results show that the proposed watermarking scheme is very robust against geometric distortion such as rotation, scaling, cropping, and affine transformation; and common signal processing such as JPEG compression, median filtering, and low-pass Gaussian filtering.

Quaternion-Based Image Hashing for Adaptive Tampering Localization

Image-hashing-based tampering detection methods have been widely studied with continuous advancements. However, most of existing models are designed for a specific tampering. In this paper, we propose a novel quaternion-based image hashing to detect almost all types of tampering, including color changing, copy move, splicing, and so on. First, the quaternion Fourier-Mellin transform is used to calculate the geometric hash to eliminate the influence of geometric distortions. Then, a new quaternion image construction method, which combines advantages of both color and structural features, is proposed to implement the quaternion Fourier transform to calculate the image feature hash to locate the tampered regions. The objective is to provide a reasonably short image hashing with good performance, i.e., being perceptually robust against various content-preserving attacks while capable of detecting and locating almost all types of tampering. Furthermore, an adaptive tampering localization algorithm is proposed based on clustering analysis to improve the detection accuracy. The experimental results show that the proposed tampering detection model outperforms the existing state-of-the-art models and is very robust against various content-preserving attacks.

Invariant Digital Image Watermarking Using Adaptive Harris Corner Detector

A robust and geometric invariant digital image watermarking scheme based on feature extraction and histogram distribution is proposed in this paper. The feature extraction method called Harris Corner Detector is adopted and revised by adjusting the response threshold value and ranking the response R value to extract feature points and thus define the regions for watermark data bits embedding and extraction. Each embedding region is a square matrix centering at the selected feature points. For watermark embedding, some pixels are moved to form a specific pattern in the intensity-level histogram distribution in each embedding region, indicating the watermark. For watermark extraction, the Adaptive Harris Corner Detector is adopted to restore the image to its original un-rotated position. According to the intensity-level histogram distribution in each embedded region, the watermark is extracted. Experimental results show that the proposed scheme is very robust against rotation, scaling, JPEG compression, median filtering, low-pass Gaussian filtering and also noise pollution.