A dataset of 1050-tampered color and grayscale images (CG-1050)

Castro, M. Pancorbo; Ballesteros, Dora M.; Renza, Diego

doi:10.1016/j.dib.2019.104864

Cited by 20 publications

(19 citation statements)

References 7 publications

(9 reference statements)

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“…All experiments were conducted on the CG-1050 database [ 49 ] which contains four datasets, each one corresponding to a different forgery technique: colorization, copy-move, splicing and retouching. The total number of forged images is 1050.…”

Section: Experimental Resultsmentioning

confidence: 99%

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Forgery Detection in Digital Images by Multi-Scale Noise Estimation

et al. 2021

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A complex processing chain is applied from the moment a raw image is acquired until the final image is obtained. This process transforms the originally Poisson-distributed noise into a complex noise model. Noise inconsistency analysis is a rich source for forgery detection, as forged regions have likely undergone a different processing pipeline or out-camera processing. We propose a multi-scale approach, which is shown to be suitable for analyzing the highly correlated noise present in JPEG-compressed images. We estimate a noise curve for each image block, in each color channel and at each scale. We then compare each noise curve to its corresponding noise curve obtained from the whole image by counting the percentage of bins of the local noise curve that are below the global one. This procedure yields crucial detection cues since many forgeries create a local noise deficit. Our method is shown to be competitive with the state of the art. It outperforms all other methods when evaluated using the MCC score, or on forged regions large enough and for colorization attacks, regardless of the evaluation metric.

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Section: Experimental Resultsmentioning

confidence: 99%

“…Images from the CG-1050 database [ 49 ] were used in this article. The full database and documentation can be downloaded from (accessed on 31 May 2021).…”

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confidence: 99%

Forgery Detection in Digital Images by Multi-Scale Noise Estimation

et al. 2021

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“…The CG-1050 [30], the Korus [31], [32], and the UMD face-swap [33] databases were used for the comparison.…”

Section: B Evaluation Datasetsmentioning

confidence: 99%

Noisesniffer: a Fully Automatic Image Forgery Detector Based on Noise Analysis

Gardella

Musé

Morel

et al. 2021

2021 IEEE International Workshop on Biometrics and Forensics (IWBF)

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Images undergo a complex processing chain from the moment light reaches the camera's sensor until the final digital image is delivered. Each of these operations leave traces on the noise model which enable forgery detection through noise analysis. In this article we define a background stochastic model which makes it possible to detect local noise anomalies characterized by their number of false alarms. The proposed method is both automatic and blind, allowing quantitative and subjectivity-free detections. Results show that the proposed method outperforms the state of the art.

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“…The most common and popularly used image forgery datasets that are publicly available for research are CASIA v1.0 [32], CASIA v2.0 [32], MICC-F220 [33], MICC-F2000 [33], Columbia Colour [34], COVERAGE [35], and CG-1050 v2 [36]. More recently, some large-scale benchmark datasets have been released to facilitate the work in image forensics like DEFACTO [37] and MFC [38], [39] datasets.…”

Section: Datasets Descriptionmentioning

confidence: 99%

“…• Controlled-environment Datasets: Datasets such as CA-SIA v1.0 [32], CASIA v2.0 [32], MICC-F220 [33], MICC-F2000 [33], Columbia Color [34], COVERAGE [35], CG-1050 v2 [36], DEFACTO [37] and MFC [38], [39]. These are the commonly used standard datasets, annotated by academicians of the community to serve -Large Scale Datasets: Having ≥ 1400 images.…”

Section: Datasets Descriptionmentioning

confidence: 99%

Analysing Statistical methods for Automatic Detection of Image Forgery

Masud¹,

Agarwal²

2021

Preprint

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Image manipulation and forgery detection have been a topic of research for more than a decade now. New-age tools and large-scale social platforms have given space for manipulated media to thrive. These media can be potentially dangerous and thus innumerable methods have been designed and tested to prove their robustness in detecting forgery. However, the results reported by state-of-the-art systems indicate that supervised approaches achieve almost perfect performance but only with particular datasets. In this work, we analyze the issue of out-ofdistribution generalisability of the current state-of-the-art image forgery detection techniques through several experiments. Our study focuses on models that utilise handcrafted features for image forgery detection. We show that the developed methods fail to perform well on cross-dataset evaluations and in-the-wild manipulated media. As a consequence, a question is raised about the current evaluation and overestimated performance of the systems under consideration.Note: This work was done during summer research internship at ITMR Lab, IIIT-Allahabad under supervision of Prof. Anupam Agarwal.

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A dataset of 1050-tampered color and grayscale images (CG-1050)

Cited by 20 publications

References 7 publications

Forgery Detection in Digital Images by Multi-Scale Noise Estimation

Forgery Detection in Digital Images by Multi-Scale Noise Estimation

Noisesniffer: a Fully Automatic Image Forgery Detector Based on Noise Analysis

Analysing Statistical methods for Automatic Detection of Image Forgery

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