Copy-Move Tampering: Some New Approaches of the Detection and Localization in a Digital Image

Abstract

Images speak. They tell us stories. Digital images carry plethora of information. The availability of cost-effective digital camera-enabled devices has made capturing images a child’s play. Statistics witnesses that the use of social networking sites has influenced people’s appetite for digital images. Billions and billions of photos are uploaded, shared and forwarded on these platforms. This makes every user an active source of the digital information. The availability of easy-to-use image editing software helps novice and experts as well capable of creating realistic alterations in these digital images. These alterations could be harmless changes for fun or serious image tampering with malicious intentions. This fact raises eyebrows and questions the authenticity of a digital image. When these digital images are used for specific purposes like news broadcasts, research publications, sports, entertainment, fashion, advertisements, legal proceedings etc., this problem becomes more critical and challenging. Therefore, digital image tampering has since long attracted the research community of image processing. Among various tampering operations, copy-move tampering is one of the easiest approaches and therefore the most common approach. In the copy-move tampering process, copying and pasting are done on the same image. Hence colour, noise component, intensity range and other properties of the image remain almost unchanged. It makes tampering detection difficult when no clue about the tampering is available other than the image itself. Further, to camouflage tampering some tricks to hide the footprint of tampering are used, such as blurring of the edges of the copy-pasted pat of the image. Technically this can be achieved by some image processing methods, e.g., JPEG compression, addition of Gaussian noise, brightness change, colour reduction, contrast adjustment etc. Occasionally some geometrical transformations such as scaling and rotation of the copied regions before pasting them somewhere else in the same image are also noticed. All these make the tampering detection a challenging task. Our study focuses on copy-move tampering detection in a digital image, either simply, i.e., without any post-processing trick or affected with different geometrical transformations and image processing methods. We first look into the tampering detection, i.e., identification and localization, using block-based technique. The first two approaches of this thesis are those, one uses LPP (Locality Preserving Projection) and the second is based on NPE (Neighborhood Preserving Embedding). Both are dimensionality reduction techniques while preserving the information of neighborhood. We find that LPP based approach worked well for simple copy-move tampering but performed poorly in case of multiple copy-move tampering and for images with self-similar structures such as some historical monuments. NPE based approach showed considerable improvement in simple copy-move images with post-processing and multiple copy-move tampering detection, however, it could not nail the tampering detection in case of self-similar images. Also, the block-based technique happens to be computationally expensive since it does a pixel-by-pixel comparison in search of a detailed clue of the tampered regions. When the copy-move region is affected with geometrical transformation, one needs a more robust clue for tampering detection. This clue must be rotation and scale-invariant. This made us concentrate on the keypoint based approach for the simple reason that image keypoints are geometrical transformation invariant. We propose to use a combination of the CenSurE keypoint detector and FREAK descriptor, which detects tampering when the image also undergoes change through scale or rotation or both following a copy-move attempt. We find that this approach also works well for simple and multiple copy-move tampering detection like in case of our two block-based approaches. The problem occurs when an image has only a few keypoints. It is observed in case of smooth images of natural landscape such as images of sky or sea or a uniform field etc. To address such situation, we propose our fourth and last approach which is based on CNN (Convolution Neural Network) and image keypoints. We have combined image information generated by CNN and CenSurE keypoints to detect and localize copy-move tampered regions. This approach enables tampering detection when the copy-move region is affected with different post-processing and geometrical transformations even in case of varying textures like smooth, coarse, or highly textured images. All these four approaches are discussed in this thesis in detail. We used several standard datasets available in public domain for performing exhaustive experiments. These are CMFD, GRIP, and CoMoFoD, MICC-F600, MICC-F220, Coverage and CASIA-II datasets. Comparison of results with some of the recently reported results of other research groups help us conclude that our approaches perform better in most of the cases and remain comparable in rest. We also discuss the future scope of our work.