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.
Other resampling detectors include @cite_51 @cite_40 @cite_52 @cite_22 @cite_30 @cite_14 @cite_50 . @cite_51 , periodic properties of interpolation were found using the second-derivative of the image and these properties were used for detecting image manipulation. Resampling on JPEG compressed images was detected in @cite_49 @cite_52 by adding noise before passing the image through the resampling detector and they showed that noise addition improved resampling detection. A normalized energy feature was implemented in @cite_18 @cite_30 and a support vector machine (SVM) was subsequently used to classify resampled images. Furthermore, recent approaches to reduce the effects of JPEG artifacts were developed in @cite_28 @cite_50 .
@cite_30 @cite_18 @cite_14 @cite_22 @cite_28 @cite_52 @cite_40 @cite_50 @cite_49 @cite_51
2532188585 2716368418 1992300492 2159244236
Resampling detection has become a standard tool in digital image forensics. This paper investigates the important case of resampling detection in re-compressed JPEG images. We show how blocking artifacts of the previous compression step can help to increase the otherwise drastically reduced detection performance in JPEG compressed images. We give a formulation on how affine transformations of JPEG compressed images affect state-of-the-art resampling detectors and derive a new efficient detection variant, which better suits this relevant detection scenario. The principal appropriateness of using JPEG pre-compression artifacts for the detection of resampling in re-compressed images is backed with experimental evidence on a large image set and for a variety of different JPEG qualities. Detecting image resampling in re-compressed images is a very challenging problem. Existing approaches to image resampling detection operate by building pre-selected model to locate periodicities in linear predictor residues. Additionally, if an image was JPEG compressed before resampling, existing techniques detect tampering using the artifacts left by the pre-compression. However, state-of-the-art approaches cannot detect resampling in re-compressed images initially compressed with high quality factor. In this paper, we propose a novel deep learning approach to adaptively learn resampling detection features directly from data. To accomplish this, we use our recently proposed constrained convolutional layer. Through a set of experiments we evaluate the effectiveness of our proposed constrained convolutional neural network (CNN) to detect resampling in re-compressed images. The results of these experiments show that our constrained CNN can accurately detect resampling in re-compressed images in scenarios that previous approaches are unable to detect. Current image re-sampling detectors can reliably detect re-sampling in JPEG images only up to a Quality Factor (QF) of 95 or higher. At lower QFs, periodic JPEG blocking artifacts interfere with periodic patterns of re-sampling. We add a controlled amount of noise to the image before the re-sampling detection step. Adding noise suppresses the JPEG artifacts while the periodic patterns due to re-sampling are partially retained. JPEG images of QF range 75-90 are considered. Gaussian Uniform noise in the range of 28-24 dB is added to the image and the images thus formed are passed to the re-sampling detector. The detector outputs are averaged to get a final output from which re-sampling can be detected even at lower QFs. We consider two re-sampling detectors - one proposed by Poposcu and Farid [1], which works well on uncompressed and mildly compressed JPEG images and the other by Gallagher [2], which is robust on JPEG images but can detect only scaled images. For multiple re-sampling operations (rotation, scaling, etc) we show that the order of re-sampling matters. If the final operation is up-scaling, it can still be detected even at very low QFs. Resampling detection has become a standard tool for forensic analyses of digital images. This paper presents new variants of image transformation operations which are undetectable by resampling detectors based on periodic variations in the residual signal of local linear predictors in the spatial domain. The effectiveness of the proposed method is supported with evidence from experiments on a large image database for various parameter settings. We benchmark detectability as well as the resulting image quality against conventional linear and bicubic interpolation and interpolation with a sinc kernel. These early findings on ldquocounter-forensicrdquo techniques put into question the reliability of known forensic tools against smart counterfeiters in general, and might serve as benchmarks and motivation for the development of much improved forensic techniques.