Detecting seam carving based image resizing using local binary patterns

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

Seam-carving is used for adjusting the size of an image as well as removing objects from images. When seam-carving is applied to delete the object in the image, it may lead to the distortion of the information transmitted by the image, misleading the public, and endangering human health. Therefore, detecting the location of objects removed by seam-carving techniques is a scientific challenge to the image forensics. According to the position movement law of the related pixel in the seam-carving process, a method of the detecting and locating the objects deleted based on the block artifact grid (BAG) mispairing characteristic is proposed in this paper. Firstly, the BAG is extracted from the JPEG image; secondly, 10-dimensional features of the BAG chart is constructed; thirdly, clustering technique is applied on the extracted features of the BAG to obtain the classification results; finally, the location of the removed object is computed based on the previous classification results. Experiment result shows that the proposed method can efficiently detect and locate the object removed from an image by seam-carving. The proposed method in this paper opens up a new research direction for the location and detection of tampering, and has important application value for the intelligent forensics system based on seam carving.

Exploring the location of object deleted by seam-carving

Seam carving is a kind of content aware image retargeting algorithm and can be applied to resize and deliberately remove objects from digital images. Based on the observation that after applying an additional seam carving operation, the similarity, the energy relative error, and the difference of seam distance of original image are quite different from those of the seam-carved image, we propose and develop a new method for detecting seam carving or seam insertion of natural images without knowledge of the original image. First, we apply an additional seam carving operation to the testing image, then calculate similarity, energy relative error, and difference of seam distance between the testing image and its seam carved version. Last, we extract 11 dimensional features to detect seam carving operation to train a support vector machine classifier for recognizing whether an image is an original or it has been modified using seam-carving. Our experimental results demonstrate that our proposed forensic method achieves not only better detection rate but also lower dimensional features compared with other existing seam carved detection methods.

Detection of Seam Carved Image Based on Additional Seam Carving Behavior

Content-aware scaling is a method for image retargeting. It has been widely used in image manipulation including tampering. To improve the detection of the forgery in JPEG images, we propose to merge calibrated neighboring joint density and a rich models-based approach that was originally designed for steganalysis. A feature selection algorithm is utilized to reduce the feature dimensionality in the merged feature set. Experimental results show that the high-dimensional detector consisting of calibrated neighboring joint density and rich model features noticeably improves the detection accuracy; and the application of feature selection method to the high-dimensional detector can further improve the detection accuracy by using a much smaller and optimized feature set.

An improved approach to detecting content-aware scaling-based tampering in JPEG images

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.

Xinfa Hu

Papers

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