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
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We propose a new method to detect re-sampled imagery. The method is based on examining the normalized energy density present within windows of varying size in the second derivative of the frequency domain, and exploiting this characteristic to derive a 19-dimensional feature vector that is used to train a SVM classifier. Experimental results are reported on 7,500 raw images from the BOSS database. Comparison with prior work reveals that the proposed algorithm performs similarly for re-sampling rates greater than 1, and is superior to prior work for re-sampling rates less than 1. Experiments are performed for both bilinear and bicubic interpolation, and qualitatively similar results are observed for each. Results are also provided for the detection of re-sampled imagery that subsequently undergoes JPEG compression. Results are quantitatively similar with some small degradation in performance as the quality factor is reduced. Transform coding using the discrete cosine transform is one of the most popular techniques for image and video compression. However, at low bit rates, the coded images suffer from severe visual distortions. An innovative approach is proposed that deals with artifacts in JPEG compressed images. Our algorithm addresses all three types of artifacts which are prevalent in JPEG images: blocking, and for edges blurring, and aliasing. We enhance the quality of the image via two stages. First, we remove blocking artifacts via boundary smoothing and guided filtering. Then, we reduce blurring and aliasing around the edges via a local edge-regeneration stage. We compared the proposed algorithm with other modern JPEG artifact-removal algorithms. The results demonstrate that the proposed approach is competitive, and can in many cases outperform, competing algorithms. A common problem affecting most image resizing detection algorithms is that they are susceptible to JPEG compression. This is because JPEG introduces periodic artifacts, as it works on 8×8 blocks. We propose a novel yet counter intuitive technique to “denoise” JPEG images by adding Gaussian noise. We add a suitable amount of Gaussian noise to a resized and JPEG compressed image so that the periodicity due to JPEG compression is suppressed while that due to resizing is retained. The controlled Gaussian noise addition works better than median filtering and weighted averaging based filtering for suppressing the JPEG induced periodicity. 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. Linear transformation, such as rotation, scaling, or any combinations of geometric attacks, is among the most common forms of image manipulation. This letter proposes a forensic technique that estimates the linear transformation of an investigated image. We exploited the periodic properties of interpolation by the second-derivative of the transformed image in both the row and column directions. Both the magnitude and phase information of the derived signals were analyzed to estimate the transformation matrix accurately. Empirical evidence from a large database of manipulated images indicates the superior performance of the proposed method.