Confidence voting method ensemble applied to off-line signature verification

In this paper, a new approximation to off-line signature verification is proposed based on two-class classifiers using an expert decisions ensemble. Different methods to extract sets of local and a global features from the target sample are detailed. Also a normalization by confidence voting method is used in order to decrease the final equal error rate (EER). Each set of features is processed by a single expert, and on the other approach proposed, the decisions of the individual classifiers are combined using weighted votes. Experimental results are given using a subcorpus of the large MCYT signature database for random and skilled forgeries. The results show that the weighted combination outperforms the individual classifiers significantly. The best EER obtained were 6.3 % in the case of skilled forgeries and 2.31 % in the case of random forgeries.     

Signature Verification: Increasing Performance by a Multi-Stage System

A serial three stage multi-expert system for facing the problem of signature verification is proposed. The whole decision process is organised into successive stages, each using a very reduced set of features for recognising forgeries and providing information about the reliability of the recognition process. The first expert, adopting only a single global feature, is devoted to the elimination of random and simple forgeries. The second stage receives only those signatures not classified as false by the first stage (i.e. those signatures really genuine or forgeries reproduced in a skilled way), and adopts a single specific feature suitable for isolating skilled forgeries. Both of these two stages employ suitable criteria for estimating the reliability of the performed classification, so that, in case of uncertainty, the signature is forwarded to a final stage which takes the final decision, taking into account the decisions of the previous stages together with the corresponding reliability estimations. The proposed multi-stage automatic signature verification system has been tested on a database of signatures produced by 49 different writers. The experimental analysis highlights the effectiveness of the approach: the proposed system employing only two features, used in distinct moments of the decision process, performs better than other systems, employing larger feature set (including the features used in the proposed system) and performing classification in a single stage.     

An off-line cheque handwritten forgery detection based on feature route density matrix

The security of handwritten documents is very important in authentication systems. In this paper, a forgery detection method is proposed for verifying handwritten documents. This method proposes two types of novel features: macro and micro. Macro features extract the structure of handwritten while micro features extract more detailed information. Also, the micro features try to extract some properties similar to online properties from offline data such as pen pressure and velocity. After extracting those features a PCA is applied to them which resulted in reducing the feature vector. A simple positive classifier is used separately to detect forgeries. It is very important that the weights of this classifier have been adjusted based on positive data because it is not possible to use forgery samples in adjusting phase. To test the proposed method a Persian handwritten data set was prepared using four kinds of forgeries; random, unskilled, skilled, and mimic. This data set consists of numbers written by text as reference words. The method performance using these different reference words showed the best result in correct rejection was 87 % while the correct acceptance was 97 %. We believe the proposed method can be applied to other languages by adjusting some parameters but because it is very important to have the data in high resolution format (e.g. 1,200 dpi) and none of databases have such resolution, the method was only applied to the dataset we gathered.     

基于数码相机固有特性的篡改检测

由于现今功能强大的图像编辑软件很容易就可以得到，所以对数字图像进行操作和编辑变得非常的容易，在一幅图像中添加或移掉一个重要的人或物并且不留任何痕迹是很有可能的。如果这些篡改图像用在媒体或法律上，对社会将造成很大的影响。随着数码相机和摄像机的不断普及，验证数字图像变得越来越重要了。文章利用数码相机的两个固有特性对图像进行篡改检测，对多幅图像进行操作，实验证明有不错的检测效果。     

A new keypoint-based copy-move forgery detection for small smooth regions

Copy-move forgery is one of the most common types of image forgeries, where a region from one part of an image is copied and pasted onto another part, thereby concealing the image content in the latter region. Keypoint based copy-move forgery detection approaches extract image feature points and use local visual features, rather than image blocks, to identify duplicated regions. Keypoint based approaches exhibit remarkable performance with respect to computational cost, memory requirement, and robustness. But unfortunately, they usually do not work well if smooth background areas are used to hide small objects, as image keypoints cannot be extracted effectively from those areas. It is a challenging work to design a keypoint-based method for detecting forgeries involving small smooth regions. In this paper, we propose a new keypoint-based copy-move forgery detection for small smooth regions. Firstly, the original tampered image is segmented into nonoverlapping and irregular superpixels, and the superpixels are classified into smooth, texture and strong texture based on local information entropy. Secondly, the stable image keypoints are extracted from each superpixel, including smooth, texture and strong texture ones, by utilizing the superpixel content based adaptive feature points detector. Thirdly, the local visual features, namely exponent moments magnitudes, are constructed for each image keypoint, and the best bin first and reversed generalized 2 nearest-neighbor algorithm are utilized to find rapidly the matching image keypoints. Finally, the falsely matched image keypoints are removed by customizing the random sample consensus, and the duplicated regions are localized by using zero mean normalized cross-correlation measure. Extensive experimental results show that the newly proposed scheme can achieve much better detection results for copy-move forgery images under various challenging conditions, such as geometric transforms, JPEG compression, and additive white Gaussian noise, compared with the existing state-of-the-art copy-move forgery detection methods.     

利用传感器噪声对复制遮盖篡改的检测

由于现今功能强大的图像编辑软件很容易就可以得到,所以对数字图像进行操作和编辑变得非常的容易,在一幅图像中添加或移掉一个重要的人或物并且不留任何痕迹是很有可能的.如果这些篡改图用在媒体或法律上,对社会将造成很大的影响.随着数码相机和摄像机的不断普及,验证数码图像变得越来越重要了.利用相机的传感器噪声对复制遮盖篡改图像进行检测,并根据其模板噪声进一步确定篡改区域.对多幅图像进行操作,实验证明效果不错.     

Image forgery detection by semi-automatic wavelet soft-Thresholding with error level analysis

In this paper a method for detection of image forgery in lossy compressed digital images known as error level analysis (ELA) is presented and it’s noisy components are filtered with automatic wavelet soft-thresholding. With ELA, a lossy compressed image is recompressed at a known error rate and the absolute differences between these images, known as error levels, are computed. This method might be weakened if the image noise generated by the compression scheme is too intense, creating the necessity of noise filtering. Wavelet thresholding is a proven denoising technique which is capable of removing an image’s noise avoiding altering other components, like high frequencies regions, by thresholding the wavelet transform coefficients, thus not causing blurring. Despite its effectiveness, the choice of the threshold is a known issue. However there are some approaches to select it automatically. In this paper, a lowpass filter is implemented through wavelet thresholding, attenuating error level noises. An efficient method to automatically determine the threshold level is used, showing good results in threshold selection for the presented problems. This automatic threshold level can be fine tuned by a parameter k. Standard test images have been doctored to simulate image tampering, error levels for these images are computed and wavelet thresholding is performed to attenuate noise. Results are presented, confirming the method’s efficiency at noise filtering while preserving necessary error levels. The main contribution of this paper is the investigation of Daubechies wavelets with semi-automatic soft-thresholding in order to highlight forgeries in images. These results can be further extended by expert systems to classify and identify forgeries.     

Keypoint-based copy-move detection scheme by adopting MSCRs and improved feature matching

Copy-move detection is to find the existence of duplicated regions in an image. In this paper, an effective method based on region features is proposed to detect copy-move forgeries, especially when the image is multiple copied or with multiple copy-move groups. Firstly, maximally stable color region detector is applied to extract features, and these features are represented by Zernike moments. Then an improved matching strategy considering n best-matching features is applied to deal with the multiple-copied problem. Moreover, a hierarchical cluster algorithm is developed to estimate transformation matrices and confirm the existence of forgery. Based on these matrices, the duplicated regions can be located at pixel level. Experimental results indicate that the proposed scheme outperforms other similar state-of-the-art techniques.     

Efficient on-line signature recognition based on multi-section vector quantization

This paper proposes a multi-section vector quantization approach for on-line signature recognition. We have used a database of 330 users which includes 25 skilled forgeries performed by 5 different impostors. This database is larger than those typically used in the literature. Nevertheless, we also provide results from the SVC database. Our proposed system obtains similar results as the state-of-the-art online signature recognition algorithm, Dynamic Time Warping, with a reduced computational requirement, around 47 times lower. In addition, our system improves the database storage requirements due to vector compression, and is more privacy-friendly because it is not possible to recover the original signature using the codebooks. Experimental results reveal that our proposed multi-section vector quantization achieves a 98% identification rate, minimum Detection Cost Function value equal to 2.29% for random forgeries and 7.75% for skilled forgeries.     

Growing random forest on deep convolutional neural networks for scene categorization

Breakthrough performances have been achieved in computer vision by utilizing deep neural networks. In this paper we propose to use random forest to classify image representations obtained by concatenating multiple layers of learned features of deep convolutional neural networks for scene classification. Specifically, we first use deep convolutional neural networks pre-trained on the large-scale image database Places to extract features from scene images. Then, we concatenate multiple layers of features of the deep neural networks as image representations. After that, we use random forest as the classifier for scene classification. Moreover, to reduce feature redundancy in image representations we derived a novel feature selection method for selecting features that are suitable for random forest classification. Extensive experiments are conducted on two benchmark datasets, i.e. MIT-Indoor and UIUC-Sports. Obtained results demonstrated the effectiveness of the proposed method. The contributions of the paper are as follows. First, by extracting multiple layers of deep neural networks, we can explore more information of image contents for determining their categories. Second, we proposed a novel feature selection method that can be used to reduce redundancy in features obtained by deep neural networks for classification based on random forest. In particular, since deep learning methods can be used to augment expert systems by having the systems essentially training themselves, and the proposed framework is general, which can be easily extended to other intelligent systems that utilize deep learning methods, the proposed method provide a potential way for improving performances of other expert and intelligent systems.     

面向图像篡改取证的多特征融合U形深度网络

随着图像篡改工具的智能化发展,图像篡改不再局限于拼接、移除等某一具体的类型,往往包含多种篡改类型及其组合操作,使得图像篡改取证工作更具挑战性。提出一种端到端的多特征融合U形深度网络,利用编解码网络提取篡改区域与真实区域之间的对比度差异、边缘差异等篡改痕迹,并使用富隐写模型卷积层获取伪造图像的噪声分布不规律信息,从而在无预处理的情况下实现可疑区域的检测并分割出高置信度的篡改区域。在此基础上,使用特征提取模块获取融合的篡改特征,在融合定位模块中利用分级监督策略融合不同分辨率提取的篡改特征,以准确定位篡改区域,实现篡改区域检测与像素级的分割。实验结果表明,基于所提网络的图像篡改取证方法在NIST16和CASIA数据库上的F1值分别为0.841和0.605,与基于MFCN、RGB-N、MANTRA-net等网络的图像篡改取证方法相比,有较优的检测性能和较高的实时性,且对JPEG压缩、缩放等处理具有更强的鲁棒性。     

Multiscale Local Gabor Phase Quantization for image forgery detection

Image Forgery is a field that has attracted the attention of a significant number of researchers in the recent years. The widespread popularity of imagery applications and the advent of powerful and inexpensive cameras are among the numerous reasons that have contributed to this upward spike in the reach of image manipulation. A considerable number of features – including numerous texture features – have been proposed by various researchers for identifying image forgery. However, detecting forgery in images utilizing texture-based features have not been explored to its full potential – especially a thorough evaluation of the texture features have not been proposed. In this paper, features based on image textures are extracted and combined in a specific way to detect the presence of image forgery. First, the input image is converted to YCbCr color space to extract the chroma channels. Gabor Wavelets and Local Phase Quantization are subsequently applied to these channels to extract the texture features at different scales and orientations. These features are then optimized using Non-negative Matrix Factorization (NMF) and fed to a Support Vector Machine (SVM) classifier. This method leads to the classification of images with accuracies of 99.33%, 96.3%, 97.6%, 85%, and 96.36% for the CASIA v2.0, CASIA v1.0, CUISDE, IFS-TC and Unisa TIDE datasets respectively showcasing its ability to identify image forgeries under varying conditions. With CASIA v2.0, the detection accuracy outperforms the recent state-of-the-art methods, and with the other datasets, it gives a comparable performance with much reduced feature dimensions.     

基于彩色LBP的隐蔽性复制-粘贴篡改盲鉴别算法

现有的复制-粘贴盲鉴别算法大多忽略图像彩色信息,导致对隐蔽性篡改方式的检测率较低,基于此,本文提出一种基于彩色局部二值模式（Color local binary patterns,CoLBP）的隐蔽性复制-粘贴盲鉴别算法.算法首先对彩色图像进行预处理,即建立彩色LBP纹理图像,从而实现彩色信息与LBP纹理特征的融合;其次重叠分块并提取灰度共生矩阵（Gray level co-occurrence matrix,GLCM）特征;最后,提出改进的kd树和超平面划分标记split搜索方法,快速匹配图像块,并应用形态学操作去除误匹配,精确定位复制-粘贴区域.实验结果表明,本算法对隐蔽性复制-粘贴篡改定位准确,并对模糊、噪声、JPEG重压缩后处理操作有很好的鲁棒性.     

Accurate and robust localization of duplicated region in copy–move image forgery

Copy–move image forgery detection has recently become a very active research topic in blind image forensics. In copy–move image forgery, a region from some image location is copied and pasted to a different location of the same image. Typically, post-processing is applied to better hide the forgery. Using keypoint-based features, such as SIFT features, for detecting copy–move image forgeries has produced promising results. The main idea is detecting duplicated regions in an image by exploiting the similarity between keypoint-based features in these regions. In this paper, we have adopted keypoint-based features for copy–move image forgery detection; however, our emphasis is on accurate and robust localization of duplicated regions. In this context, we are interested in estimating the transformation (e.g., affine) between the copied and pasted regions more accurately as well as extracting these regions as robustly by reducing the number of false positives and negatives. To address these issues, we propose using a more powerful set of keypoint-based features, called MIFT, which shares the properties of SIFT features but also are invariant to mirror reflection transformations. Moreover, we propose refining the affine transformation using an iterative scheme which improves the estimation of the affine transformation parameters by incrementally finding additional keypoint matches. To reduce false positives and negatives when extracting the copied and pasted regions, we propose using “dense” MIFT features, instead of standard pixel correlation, along with hysteresis thresholding and morphological operations. The proposed approach has been evaluated and compared with competitive approaches through a comprehensive set of experiments using a large dataset of real images (i.e., CASIA v2.0). Our results indicate that our method can detect duplicated regions in copy–move image forgery with higher accuracy, especially when the size of the duplicated region is small.     

Reducing forgeries in writer-independent off-line signature verification through ensemble of classifiers

In this work we address two important issues of off-line signature verification. The first one regards feature extraction. We introduce a new graphometric feature set that considers the curvature of the most important segments, perceptually speaking, of the signature. The idea is to simulate the shape of the signature by using Bezier curves and then extract features from these curves. The second important aspect is the use of an ensemble of classifiers based on graphometric features to improve the reliability of the classification, hence reducing the false acceptance. The ensemble was built using a standard genetic algorithm and different fitness functions were assessed to drive the search. Two different scenarios were considered in our experiments. In the former, we assume that only genuine signatures and random forgeries are available to guide the search. In the latter, on the other hand, we assume that simple and simulated forgeries also are available during the optimization of the ensemble. The pool of base classifiers is trained using only genuine signatures and random forgeries. Thorough experiments were conduct on a database composed of 100 writers and the results compare favorably.     

Online Signature Verification and Recognition: An Approach Based on Symbolic Representation

In this paper, we propose a new method of representing on-line signatures by interval valued symbolic features. Global features of on-line signatures are used to form an interval valued feature vectors. Methods for signature verification and recognition based on the symbolic representation are also proposed. We exploit the notions of writer dependent threshold and introduce the concept of feature dependent threshold to achieve a significant reduction in equal error rate. Several experiments are conducted to demonstrate the ability of the proposed scheme in discriminating the genuine signatures from the forgeries. We investigate the feasibility of the proposed representation scheme for signature verification and also signature recognition using all 16500 signatures from 330 individuals of the MCYT bimodal biometric database. Further, extensive experimentations are conducted to evaluate the performance of the proposed methods by projecting features onto Eigenspace and Fisherspace. Unlike other existing signature verification methods, the proposed method is simple and efficient. The results of the experimentations reveal that the proposed scheme outperforms several other existing verification methods including the state-of-the-art method for signature verification.     

Blind Authentication Using Periodic Properties of Interpolation

In this paper, we analyze and analytically describe the specific statistical changes brought into the covariance structure of signal by the interpolation process. We show that interpolated signals and their derivatives contain specific detectable periodic properties. Based on this, we propose a blind, efficient, and automatic method capable of finding traces of resampling and interpolation. The proposed method can be very useful in many areas, especially in image security and authentication. For instance, when two or more images are spliced together, to create high quality and consistent image forgeries, almost always geometric transformations, such as scaling, rotation, or skewing are needed. These procedures are typically based on a resampling and interpolation step. By having a method capable of detecting the traces of resampling, we can significantly reduce the successful usage of such forgeries. Among other points, the presented method is also very useful in estimation of the geometric transformations factors.      

Consistency features and fuzzy-based segmentation for shadow and reflection detection in digital image forgery

Advances in photo editing software have made it possible to generate visually convincing photo-graphic forgeries which have been increased tremendously in recent years. In order to alleviate the problem of image forgery, a handful of techniques have been presented in the literature to detect forgery either in shadow or reflection. This paper aims to develop a technique to detect the image forgery either in shadow or reflection using features enabled neural network. The proposed technique of image forgery detection contains three important steps, like segmentation, feature extraction and detection. In segmentation, shadow points and reflection points are identified using map-based segmentation and FCM clustering. Then, feature points from the shadow points and reflective parts are extracted by considering texture consistency and strength consistency using LVP operator. The final step of forgery detection is performed using the feed forward neural network, where a new algorithm called ABCLM is developed for training of neural network weights. The performance is analyzed with four existing algorithms using measures such as accuracy and MSE. From the analysis, we understand that the proposed technique obtained the maximum accuracy of 80.49%.