Raise

Dang-Nguyen, Duc-Tien; Pasquini, Cecilia; Conotter, Valentina; Boato, Giulia

doi:10.1145/2713168.2713194

Cited by 376 publications

(54 citation statements)

References 21 publications

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“…In our experiments, camera-native (uncompressed) images were taken from the RAISE dataset [19]. We also used uncompressed images from the Dresden Image Database [20] for additional testing.…”

Section: Resultsmentioning

confidence: 99%

Higher-order, adversary-aware, double JPEG-detection via selected training on attacked samples

Bami

Nowroozi

Tondi

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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Abstract-In this paper we present an adversary-aware double JPEG detector which is capable of detecting the presence of two JPEG compression steps even in the presence of heterogeneous processing and counter-forensic (C-F) attacks. The detector is based on an SVM classifier fed with a large number of features and trained to recognise the traces left by double JPEG detection in the presence of attacks. Since it is not possible to train the SVM on all possible kinds of processing and C-F attacks, a selected set of images, manipulated with a limited number of attacks is added to the training set. The processing tools used for training are chosen among those that proved to be most effective in disabling double JPEG detection. Experimental results prove that training on such a kind of most powerful attacks allows good detection in the presence of a much wider variety of attacks and processing. Good performance are retained over a wide range of compression quality factors.

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Section: Resultsmentioning

confidence: 99%

Higher-order, adversary-aware, double JPEG-detection via selected training on attacked samples

Bami

Nowroozi

Tondi

2017

2017 25th European Signal Processing Conference (EUSIPCO)

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“…We considered the RAISE dataset [22] with 8156 native (tiff ) images, that is split into a training and a test set. Specifically, 7000 images were considered for training, while the remaining 1156 were reserved for testing.…”

Section: Datasetsmentioning

confidence: 99%

“…The datasets used for the experiments are publicly available in [22] and [23]. The code is available at the following Github link: https://github.com/ andreacos/BoostingCNN-Jpeg-Primary-Quantization-Matrix-Estimation.…”

Section: Availability Of Data and Materialsmentioning

confidence: 99%

Boosting CNN-based primary quantization matrix estimation of double JPEG images via a classification-like architecture

Tondi

Costanzo

Huang

et al. 2021

EURASIP J. on Info. Security

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Estimating the primary quantization matrix of double JPEG compressed images is a problem of relevant importance in image forensics since it allows to infer important information about the past history of an image. In addition, the inconsistencies of the primary quantization matrices across different image regions can be used to localize splicing in double JPEG tampered images. Traditional model-based approaches work under specific assumptions on the relationship between the first and second compression qualities and on the alignment of the JPEG grid. Recently, a deep learning-based estimator capable to work under a wide variety of conditions has been proposed that outperforms tailored existing methods in most of the cases. The method is based on a convolutional neural network (CNN) that is trained to solve the estimation as a standard regression problem. By exploiting the integer nature of the quantization coefficients, in this paper, we propose a deep learning technique that performs the estimation by resorting to a simil-classification architecture. The CNN is trained with a loss function that takes into account both the accuracy and the mean square error (MSE) of the estimation. Results confirm the superior performance of the proposed technique, compared to the state-of-the art methods based on statistical analysis and, in particular, deep learning regression. Moreover, the capability of the method to work under general operative conditions, regarding the alignment of the second compression grid with the one of first compression and the combinations of the JPEG qualities of former and second compression, is very relevant in practical applications, where these information are unknown a priori.

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“…More recently, RAISE (RAw ImageS datasEt) was presented [13]: it is a collection of 8156 raw images including a wide variety of both semantic contents and technical parameters. Three different devices (a Nikon D40, a Nikon D90, and a Nikon D7000) are employed, and the images are taken at very high resolution (3008×2000, 4288×2848, and 4928×3264 pixels) and saved in an uncompressed format (Compress Raw 12-bit and Lossless Compress Raw 14-bit) as natively provided by the employed cameras.…”

Section: Motivationmentioning

confidence: 99%

VISION: a video and image dataset for source identification

Shullani

Fontani

Iuliani

et al. 2017

EURASIP J. on Info. Security

218

175

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Forensic research community keeps proposing new techniques to analyze digital images and videos. However, the performance of proposed tools are usually tested on data that are far from reality in terms of resolution, source device, and processing history. Remarkably, in the latest years, portable devices became the preferred means to capture images and videos, and contents are commonly shared through social media platforms (SMPs, for example, Facebook, YouTube, etc.). These facts pose new challenges to the forensic community: for example, most modern cameras feature digital stabilization, that is proved to severely hinder the performance of video source identification technologies; moreover, the strong re-compression enforced by SMPs during upload threatens the reliability of multimedia forensic tools. On the other hand, portable devices capture both images and videos with the same sensor, opening new forensic opportunities. The goal of this paper is to propose the VISION dataset as a contribution to the development of multimedia forensics. The VISION dataset is currently composed by 34,427 images and 1914 videos, both in the native format and in their social version (Facebook, YouTube, and WhatsApp are considered), from 35 portable devices of 11 major brands. VISION can be exploited as benchmark for the exhaustive evaluation of several image and video forensic tools.

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Raise

Cited by 376 publications

References 21 publications

Higher-order, adversary-aware, double JPEG-detection via selected training on attacked samples

Higher-order, adversary-aware, double JPEG-detection via selected training on attacked samples

Boosting CNN-based primary quantization matrix estimation of double JPEG images via a classification-like architecture

VISION: a video and image dataset for source identification

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