Camera model identification has gained significant importance in image forensics as digitally altered images are becoming increasingly commonplace. In this paper, we present a solution to the problem of identifying the source camera model of an image using a novel deep learning architecture called Remnant Convolutional Neural Network (RemNet). RemNet is comprised of multiple remnant blocks with intra-block skip connection and a classification block in series. Unlike the conventional fixed filters used in image forensics for preprocessing, our proposed novel remnant blocks are completely data driven. It suppresses unnecessary image contents dynamically and generates a remnant of the image from where the classification block can extract intrinsic camera model-specific features for model identification. The whole architecture is trained end-to-end. This network proves to be very robust for identifying the source camera model, even if the original images are post-processed. The network, trained and tested on 18 models from Dresden database, shows 100% accuracy for 16 camera models with an overall accuracy of 97.59% where the test dataset consisted of images from unseen devices. This result is better in comparison to other state of the art methods. Our network also achieves an overall accuracy of 95.01% on the IEEE Signal Processing (SP) Cup 2018 dataset, which indicates the generalizability of our network. In addition, RemNet achieves an overall accuracy of 99.53% in image manipulation detection which implies that it can be used as a general purpose network for image forensic tasks.
Genomes encode for genes and the regulatory signals that enable those genes to be transcribed, and are continually shaped by evolution. Genomes, including those of human and yeast, encode for numerous regulatory elements and transcripts that have limited evidence of conservation or function. Here, we sought to create a genomic null hypothesis by quantifying the gene regulatory activity of evolutionarily naïve DNA, using RNA-seq of evolutionarily distant DNA expressed in yeast and computational predictions of random DNA activity in human cells and tissues. In yeast, we found that >99% of bases in naïve DNA expressed as part of one or more transcripts. Naïve transcripts are sometimes spliced, and are similar to evolved transcripts in length and expression distribution, indicating that stable expression and/or splicing are insufficient to indicate adaptation. However, naïve transcripts do not achieve the extreme high expression levels as achieved by evolved genes, and frequently overlap with antisense transcription, suggesting that selection has shaped the yeast transcriptome to achieve high expression and coherent gene structures. In humans, we found that, while random DNA is predicted to have minimal activity, dinucleotide content-matched randomized DNA is predicted to have much of the regulatory activity of evolved sequences, including active chromatin marks at between half (DNase I and H3K4me3) and 1/16th (H3K27ac and H3K4me1) the rate of evolved DNA, and the repression-associated H3K27me3 at about twice the rate of evolved DNA. Naïve human DNA is predicted to be more cell type-specific than evolved DNA and is predicted to generate co-occurring chromatin marks, indicating that these are not reliable indicators of selection. However, extreme high activity is rarely achieved by naïve DNA, consistent with these arising via selection. Our results indicate that evolving regulatory activity from naïve DNA is comparatively easy in both yeast and humans, and we expect to see many biochemically active and cell type-specific DNA sequences in the absence of selection. Such naïve biochemically active sequences have the potential to evolve a function or, if sufficiently detrimental, selection may act to repress them.
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