One of the principal problems in image forensics is determining if a particular image is authentic or not. This can be a crucial task when images are used as basic evidence to influence judgment like, for example, in a court of law. To carry out such forensic analysis, various technological instruments have been developed in the literature. In this paper, the problem of detecting if an image has been forged is investigated; in particular, attention has been paid to the case in which an area of an image is copied and then pasted onto another zone to create a duplication or to cancel something that was awkward. Generally, to adapt the image patch to the new context a geometric transformation is needed. To detect such modifications, a novel methodology based on scale invariant features transform (SIFT) is proposed. Such a method allows us to both understand if a copy-move attack has occurred and, furthermore, to recover the geometric transformation used to perform cloning. Extensive experimental results are presented to confirm that the technique is able to precisely individuate the altered area and, in addition, to estimate the geometric transformation parameters with high reliability. The method also deals with multiple cloning.

A SIFT-Based Forensic Method for Copy–Move Attack Detection and Transformation Recovery

A copy-move forgery is created by copying and pasting content within the same image, and potentially postprocessing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features Sift and Surf, as well as the block-based DCT, DWT, KPCA, PCA, and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.

An Evaluation of Popular Copy-Move Forgery Detection Approaches

A copy-move forgery is created by copying and pasting content within the same image, and potentially post-processing it. In recent years, the detection of copy-move forgeries has become one of the most actively researched topics in blind image forensics. A considerable number of different algorithms have been proposed focusing on different types of postprocessed copies. In this paper, we aim to answer which copy-move forgery detection algorithms and processing steps (e.g., matching, filtering, outlier detection, affine transformation estimation) perform best in various postprocessing scenarios. The focus of our analysis is to evaluate the performance of previously proposed feature sets. We achieve this by casting existing algorithms in a common pipeline. In this paper, we examined the 15 most prominent feature sets. We analyzed the detection performance on a per-image basis and on a per-pixel basis. We created a challenging real-world copy-move dataset, and a software framework for systematic image manipulation. Experiments show, that the keypoint-based features SIFT and SURF, as well as the block-based DCT, DWT, KPCA, PCA and Zernike features perform very well. These feature sets exhibit the best robustness against various noise sources and downsampling, while reliably identifying the copied regions.

Region duplication is a simple and effective operation to create digital image forgeries, where a continuous portion of pixels in an image, after possible geometrical and illumination adjustments, are copied and pasted to a different location in the same image. Most existing region duplication detection methods are based on directly matching blocks of image pixels or transform coefficients, and are not effective when the duplicated regions have geometrical or illumination distortions. In this work, we describe a new region duplication detection method that is robust to distortions of the duplicated regions. Our method starts by estimating the transform between matched scale invariant feature transform (SIFT) keypoints, which are insensitive to geometrical and illumination distortions, and then finds all pixels within the duplicated regions after discounting the estimated transforms. The proposed method shows effective detection on an automatically synthesized forgery image database with duplicated and distorted regions. We further demonstrate its practical performance with several challenging forgery images created with state-of-the-art tools.

Region Duplication Detection Using Image Feature Matching

http://www.dxsx.net/admin/editor/upload/201412/2014122915411949.pdf

Digital image forgery detection using passive techniques: A survey

This paper proposes a method for detecting copy-move forgery over images tampered by copy-move. To detect such forgeries, the given image is divided into overlapping blocks of equal size, feature for each block is then extracted and represented as a vector, all the extracted feature vectors are then sorted using the radix sort. The difference (shift vector) of the positions of every pair of adjacent feature vectors in the sorting list is computed. The accumulated number of each of the shift vectors is evaluated. A large accumulated number is considered as possible presence of a duplicated region, and thus all the feature vectors corresponding to the shift vectors with large accumulated numbers are detected, whose corresponding blocks are then marked to form a tentative detected result. Finally, the medium filtering and connected component analysis are performed on the tentative detected result to obtain the final result. Compared with other methods, employing the radix sort makes the detection much more efficient without degradation of detection quality.

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Fast copy-move forgery detection

In this paper, we propose an effective method for detecting linear up-sampling in images. Our method includes three parts. First, we present an algorithm, Resampling Matrix Construction, that automatically derives the resampling matrix for any given resampling factor of fraction. Second, we show an algorithm, Zeroing Mask Derivation, that constructs a zeroing mask for the resampling produced by the proposed algorithm. Last, we propose an algorithm, up-sampling detection (UD), that detects the up-sampling in images using the zeroing masks in a specific order. Compared with existing methods, the proposed method for UD can not only effectively detect resampling by any fraction but can also recover the up-sampling fraction. Preliminary experimental results show that the proposed method is indeed effective.

Effective Detection for Linear Up-Sampling by a Factor of Fraction

In this paper we proposed a music matching method, called the RLCS (rough longest common subsequence) method. It is an improved version of the LCS to avoid some problems occurring in global alignment matching. First a rough equality for two notes is defined for constructing the RLCS of two music fragments. The length of the RLCS of two music sequences defined in this work is a real number, called a weighted length. It is evaluated according to the degree of similarity of every pair of matched notes from the two sequences. This method takes into account both the width-across-query (WAQ) and the width-across-reference (WAR) and combines them with the weighted length of the corresponding RLCS to define a score measurement for the RLCS. The measurement associated with WAQ and WAR enables the proposed method to tolerate dense errors. A dynamic programming algorithm is presented for simultaneously calculating the weighted length of RLCS, the WAQ, the WAR, and the score to determine the RLCS. As a result, the proposed method can perform the matching in a better and simpler manner. In order to speed up the matching process, we use the filtering algorithm proposed by Tarhio and Ukkonen [22] to filter the reference and discard most of the reference areas that do not match. We applied the proposed algorithm to content-based music retrieval. The experimental results showed that with our proposed algorithm the retrieval system provides a higher retrieval rate than that with the local alignment method proposed by Suyoto et al. [20]. The use of the filtering algorithm has been shown to greatly reduce the computation time for exact matching and for approximate matching with a low error tolerance.

Music Matching Based on Rough Longest Common Subsequence

We propose a method to extract text information from video sequences. First the frequency of high horizontal energy in a video frame is examined to extract text blocks. Structural operations are then performed to remove the background so that the text can be extracted for later recognition. Experiments show that the method is efficient and effective for extracting text from various video documents.

Text Extraction in Video Images

This paper presents a robust and efficient text detection algorithm for news video. The proposed algorithm uses the temporal information of video and logical AND operation to remove most of irrelevant background. Then a window-based method by counting the black-and-white transitions is applied on the resulted edge map to obtain rough text blobs. Line deletion technique is used twice to refine the text blocks. The proposed algorithm is applicable to multiple languages (English, Japanese and Chinese), robust to text polarities (positive or negative), various character sizes (from 4×7 to 30×30), and text alignments (horizontal or vertical). Three metrics, recall (R), precision (P), and quality of bounding preciseness (Q), are adopted to measure the efficacy of text detection algorithms. According to the experimental results on various multilingual video sequences, the proposed algorithm has a 96% and above performance in all three metrics. Comparing to existing methods, our method has better performance especially in the quality of bounding preciseness that is crucial to later binarization process.

/pdf/robust-news-video-text-detection-based-on-edges-and-line-5hufemp3va.pdf

Chun-Wei Wang

Papers

Fast copy-move forgery detection

Effective Detection for Linear Up-Sampling by a Factor of Fraction

Music Matching Based on Rough Longest Common Subsequence

Text Extraction in Video Images

Robust news video text detection based on edges and line-deletion