Boosting Image Forgery Detection using Resampling Features and Copy-move Analysis

Realistic image forgeries involve a combination of splicing, resampling, cloning, region removal and other methods. While resampling detection algorithms are effective in detecting splicing and resampling, copy-move detection algorithms excel in detecting cloning and region removal. In this paper, we combine these complementary approaches in a way that boosts the overall accuracy of image manipulation detection. We use the copy-move detection method as a pre-filtering step and pass those images that are classified as untampered to a deep learning based resampling detection framework. Experimental results on various datasets including the 2017 NIST Nimble Challenge Evaluation dataset comprising nearly 10,000 pristine and tampered images shows that there is a consistent increase of 8%-10% in detection rates, when copy-move algorithm is combined with different resampling detection algorithms.  Fake images are becoming a growing threat to information reliability. With the ubiquitous availability of various powerful image editing software tools and smartphone apps such as Photoshop, GIMP, Snapseed and Pixlr, it has become very trivial to manipulate digital images. The field of Digital Image Forensics aims to develop tools that can identify the authenticity of digital images and localize regions in an image which have been tampered with. There are many types of image forgeries such as splicing objects from one image to another, removing objects or regions from images, creating copies of objects in the same image, and more. To detect these forgeries, researchers have proposed methods based on several techniques such as JPEG compression artifacts, resampling detection, lighting artifacts, noise inconsistencies, camera sensor noise, and many more. However, most techniques in literature focus on a specific type of manipulation or a groups of similar tamper operations. In realistic scenarios, a host of operations are applied when creating tampered images. For example, when an object is spliced onto an image, it is often accompanied by other operations such as scaling, rotation, smoothing, contrast enhancement, and more. Very few studies address these challenging scenarios with the aid of Image Forensics challenges and competitions such as IEEE Image Forensics challenge  and the recent NIST Nimble Media Forensics challenge . These competitions try to mimic a realistic scenario and contain a large number of doctored images which involves several types of image manipulations. In order to detect the tampered images, a single detection method will not be sufficient to identify the different types of manipulations. In this paper, we demonstrate the importance of combining forgery detection algorithms, especially when the features are complementary, to boost the image manipulation detection rates. We propose a simple method to identify realistic forgeries by fusing two complementary approaches: resampling detection and copy-move detection. Our experimental results show the approach is promising and achieves an increase in detection rates. Image forgeries are usually created by splicing a portion of an image onto some other image. In the case of splicing or object removal, the tampered region is often scaled or rotated to make it proportional to the neighboring untampered area. This creates resampling of the image grid and detection of resampling indicates evidence of image manipulation. Several techniques have been proposed to detect resampling in digital images [3, 4, 5, 6, 7, 8, 9]. Similarly, copy-move forgeries are common, where a part of the image is copied and pasted on another part generally to conceal unwanted portions of the image. Detection of these copied parts indicates evidence of tampering [10, 11, 12, 13, 14, 15, 16, 17]. In this paper, we combine our previous work on resampling forgery detection [18] with a dense-field based copy-move forgery detection method developed by Cozzolino et al.  to assign a manipulation confidence score. We demonstrate that our algorithm is effective at detecting many different types of image tampering that can be used to verify the authenticity of digital images. In [18], we designed a detector based on Radon transform and deep learning. The detector found image artifacts imposed by classic upsampling, downsampling, clockwise and counter clockwise rotations, and shearing methods. We combined these five different resampling detectors with a JPEG compression detector and for each of the six detectors we output a heatmap which indicates the regions of resampling anomalies. The generated heatmaps were smoothed to localize the detection and determine the detection score. In this work, we combine the above approach with a copy-move forgery detector [16]. Our experiments demonstrate that the resampling features are complementary to the copyar X iv :1 80 2. 03 15 4v 2 [ cs .C V ] 1 9 Fe b 20 18