First Steps Toward Camera Model Identification With Convolutional Neural Networks

Bondi, Luca; Baroffio, Luca; Güera, David; Bestagini, Paolo; Delp, Edward J.; Tubaro, Stefano

doi:10.1109/lsp.2016.2641006

Cited by 226 publications

(189 citation statements)

References 31 publications

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“…In particular, f ip1 achieves the best NA, which is close to 0.83. This confirms the behavior observed by Bondi et al [15] for the closed-set scenario: hand-crafted features (i.e., f rich and f cfa ) performs better on high resolution images, whereas the CNN is superior when trained on small 64 × 64 pixel patches as the ones considered in this work. The explanation for the affected accuracy with hand-crafted features when working with small patches is that hand-crafted features relies on co-occurrences [11,25], whose computation for small patches might be less stable and reliable.…”

Section: A Feature Extractorssupporting

confidence: 90%

“…For this reason, we denote f cfa ∈ R 1372 as the CFA-based feature vector proposed by Chen and Stamm [11]. As shown by Bondi et al [15], this can be considered a baseline solution especially when large images are concerned.…”

Section: ) Cfa Featuresmentioning

confidence: 99%

“…1) Dresden Image Database [37] This dataset contains almost 17000 images from 27 different camera models. Exactly as in the work of Bondi et al [15], we selected 13000 images from 18 models 1 as the set of images from known camera models. This set was split in training, validation, and test sets [15].…”

Section: A Datasetsmentioning

confidence: 99%

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An In-Depth Study on Open-Set Camera Model Identification

et al. 2019

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Camera model identification refers to the problem of linking a picture to the camera model used to shoot it. As this might be an enabling factor in different forensic applications to single out possible suspects (e.g., detecting the author of child abuse or terrorist propaganda material), many accurate camera model attribution methods have been developed in the literature. One of their main drawbacks, however, is the typical closed-set assumption of the problem. This means that an investigated photograph is always assigned to one camera model within a set of known ones present during investigation, i.e., training time. The fact that a picture can come from a completely unrelated camera model during actual testing is usually ignored. Under realistic conditions, it is not possible to assume that every picture under analysis belongs to one of the available camera models. To deal with this issue, in this paper, we present an in-depth study on the possibility of solving the camera model identification problem in open-set scenarios. Given a photograph, we aim at detecting whether it comes from one of the known camera models of interest or from an unknown one. We compare different feature extraction algorithms and classifiers specially targeting open-set recognition. We also evaluate possible open-set training protocols that can be applied along with any open-set classifier, observing that a simple alternative among the selected ones obtains the best results. Thorough testing on independent datasets shows that it is possible to leverage a recently proposed convolutional neural network as feature extractor paired with a properly trained open-set classifier aiming at solving the open-set camera model attribution problem even on small-scale image patches, improving over state-of-the-art available solutions. INDEX TERMS Camera model identification, image forensics, open-set recognition, open-set training protocol.

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Section: A Feature Extractorssupporting

confidence: 90%

Section: ) Cfa Featuresmentioning

confidence: 99%

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An In-Depth Study on Open-Set Camera Model Identification

et al. 2019

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“…As is known, SPN has been used to identify source cameras of NIs and has obtained excellent performance [10,11,16]. However, there is no SPN in CGs.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…Deep neural networks such as the convolutional neural network (CNN) have the capacity to automatically obtain high-dimensional features and reduce its dimensionality efficiently [8]. Some researchers have begun to utilize deep learning to solve problems in the domain of image forensics, such as image manipulation detection [9], camera model identification [10,11], steganalysis [12,13], image copy-move forgery detection [14], and so on.…”

Section: Introductionmentioning

confidence: 99%

Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Yao

Zhang

et al. 2018

Sensors

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Computer-generated graphics (CGs) are images generated by computer software. The rapid development of computer graphics technologies has made it easier to generate photorealistic computer graphics, and these graphics are quite difficult to distinguish from natural images (NIs) with the naked eye. In this paper, we propose a method based on sensor pattern noise (SPN) and deep learning to distinguish CGs from NIs. Before being fed into our convolutional neural network (CNN)-based model, these images—CGs and NIs—are clipped into image patches. Furthermore, three high-pass filters (HPFs) are used to remove low-frequency signals, which represent the image content. These filters are also used to reveal the residual signal as well as SPN introduced by the digital camera device. Different from the traditional methods of distinguishing CGs from NIs, the proposed method utilizes a five-layer CNN to classify the input image patches. Based on the classification results of the image patches, we deploy a majority vote scheme to obtain the classification results for the full-size images. The experiments have demonstrated that (1) the proposed method with three HPFs can achieve better results than that with only one HPF or no HPF and that (2) the proposed method with three HPFs achieves 100% accuracy, although the NIs undergo a JPEG compression with a quality factor of 75.

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Fighting Fake News: Image Splice Detection via Learned Self-Consistency

Huh

Liu

Owens

et al. 2018

Computer Vision – ECCV 2018

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Fig. 1: Our algorithm learns to detect and localize image manipulations (splices), despite being trained only on unmanipulated images. The two input images above might look plausible, but our model correctly determined that they have been manipulated because they lack self-consistency: the visual information within the predicted splice region was found to be inconsistent with the rest of the image. IMAGE CREDITS: automatically created splice from Hays and Efros [1] (top), manual splice from Reddit user /u/Name-Albert Einstein (bottom).Abstract. Advances in photo editing and manipulation tools have made it significantly easier to create fake imagery. Learning to detect such manipulations, however, remains a challenging problem due to the lack of sufficient amounts of manipulated training data. In this paper, we propose a learning algorithm for detecting visual image manipulations that is trained only using a large dataset of real photographs. The algorithm uses the automatically recorded photo EXIF metadata as supervisory signal for training a model to determine whether an image is self-consistent -that is, whether its content could have been produced by a single imaging pipeline. We apply this self-consistency model to the task of detecting and localizing image splices. The proposed method obtains state-ofthe-art performance on several image forensics benchmarks, despite never seeing any manipulated images at training. That said, it is merely a step in the long quest for a truly general purpose visual forensics tool. Fig. 2: Anatomy of a splice: One of the most common ways of creative fake images is splicing together content from two different real source images. The insight explored in this paper is that patches from a spliced image are typically produced by different imaging pipelines, as indicated by the EXIF meta-data of the two source images. The problem is that in practice, we never have access to these source images at test time. 1

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First Steps Toward Camera Model Identification With Convolutional Neural Networks

Cited by 226 publications

References 31 publications

An In-Depth Study on Open-Set Camera Model Identification

An In-Depth Study on Open-Set Camera Model Identification

Distinguishing Computer-Generated Graphics from Natural Images Based on Sensor Pattern Noise and Deep Learning

Fighting Fake News: Image Splice Detection via Learned Self-Consistency

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