Detecting seam carving based image resizing using local binary patterns

Every day the use of images from mobile devices as evidence in legal proceedings is more usual and common.Image source acquisition identification is a branch of digital forensic analysis.We use a combination of hierarchical and flat clustering and the use of Sensor Pattern Noise for source identification.We make a series of experiments which emulate similar situations to those that may occur in reality. Every day the use of images from mobile devices as evidence in legal proceedings is more usual and common. Therefore, forensic analysis of mobile device images takes on special importance. This paper explores the branch of forensic analysis which is based on the identification of the source, specifically on the grouping or clustering of images according to their source acquisition. In contrast with other state of the art techniques for source identification, hierarchical clustering does not involve a priori knowledge of the number of images or devices to be identified or training data for a future classification stage. That is, a grouping by classes with all the input images is performed. The proposal is based on the combination of hierarchical and flat clustering and the use of Sensor Pattern Noise (SPN). There has been a series of experiments which emulate similar situations to those that may occur in reality to test the robustness and reliability of the results of the technique. The results are satisfactory in all the experiments, obtaining high rates of success.

Smartphone image clustering

Seam carving is widely used excellent content-aware image scaling method. When an image is processed by seam carving, its local texture changes. Local binary patterns is an excellent local descriptor for describing the local texture of an image. In this paper, a blind detection based uniform local binary patterns(ULBP) is proposed to detect seam-carved image. Firstly, the image is transformed into gray-scale image. Then the ULBP histogram features and seam features are extracted from the gray-scale image. Finally support vector machine (SVM) is adopted as classifier to train and test those features to identify whether an image is subjected to seam carving or not. Wei et al. (Pattern Recogn Lett 36:100–106 2014) method and Ryu et al. (IEICE Trans Inf Syst 97(5):1304–1311 2014) method are selected as the benchmark. Extensive compared experiments are conducted by the three methods, respectively. Experimental results show that the proposed method has the best performance among the three methods under a variety of setting.

Detecting seam carved images using uniform local binary patterns

The ability to relate a digital photograph to its source camera has application in the areas of digital forensics and multimedia data mining. The majority of previous research in this area has focused on primary function imaging devices (i.e. digital cameras). In this work we use the pattern noise of an imaging sensor to classify digital photographs according to the source smartphone from which they originated. This is timely work as new smartphone models large imaging sensors, afford significant improvements in classification rates using pattern noise. Our approach is to extract wavelet based features which are then classified using a support vector machine. We show that this method generalises well when the number of source cameras is increased.

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Source Smartphone Identification Using Sensor Pattern Noise and Wavelet Transform

Digital image forensics has lately become one of the very important applications to identify the characteristics and the originality of the digital devices. This paper studies the recent developments in the field of image source identification. Proposed techniques in the literature are categorized into five primary areas based on source model identification: Metadata, Image Features, CFA and Demosaicing Artifacts, Lens Distortions and Wavelet Transforms. The main idea of the proposed approaches in each category is described in detail, and reported results are discussed to evaluate the potential of the methods.

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Techniques for source camera identification

As smartphones are being widely used in daily lives, the images captured by smartphones become ubiquitous and may be used for legal purposes. Accordingly, the authentication of smartphone images and the identification of post-capture manipulation are of significant interest in digital forensics. In this paper, we propose a method to determine the smartphone camera source of a particular image and operations that may have been performed on that image. We first take images using different smartphones and purposely manipulate the images, including different combinations of double JPEG compression, cropping, and rescaling. Then, we extract the marginal density in low frequency coordinates and neighboring joint density features on intra-block and inter-block as features. Finally, we employ a support vector machine to identify the smartphone source as well as to reveal the operations. Experimental results show that our method is very promising for identifying both smartphone source and manipulations. Our study also indicates that applying unsupervised clustering and supervised classification together (clustering first, followed by classification) leads to improvement in identifying smartphone sources and manipulations and thus provides a means to address the complexity issue of intentional manipulation.

Identification of smartphone-image source and manipulation

Content-aware image resizing, also known as image retargeting, seam carving, content-aware scaling, is originally proposed to automatically remove the paths of least importance, known as seams, to reduce image size or insert seams to extend it, in order to display images without distortion on various media especially on mobile devices, such as smartphones and PDAs. Content-aware scaling also allows removing entire objects from photographs without observed clues, and hence it has been used to tamper images. Due to the ubiquity of JPEG images on various mobile devices, it is increasingly necessary to authenticate these JPEG images for legitimate purposes. To detect the content-aware-based forgery in JPEG images, in this paper, we merge shift-recompression-based characteristic features in spatial domain and shift-recompression-based neighboring joint density in DCT domain together; an ensemble classifier is used to discriminate forged JPEG images from intact JPEG images. We also transfer other popular JPEG-based steganalysis methods to detecting the forgery. Experimental results show that steganalysis methods are effective in detecting context-aware-based JPEG forgery and our method is superior to other compared detection methods.

Xiaodong Li

Papers

Identification of smartphone-image source and manipulation

Shift recompression-based feature mining for detecting content-aware scaled forgery in JPEG images