This paper describes the complete implementation of a blind image algorithm, that takes any digital image as input. In a first step the algorithm estimates a Signal and Frequency Dependent (SFD) noise model. In a second step, the image is denoised by a multiscale adaptation of the Non-local Bayes denoising method. We focus here on a careful analysis of the denoising step and present a detailed discussion of the influence of its parameters. Extensive commented tests of the blind denoising algorithm are presented, on real JPEG images and scans of old photographs.

Significance: Based on a signal-processing approach, we propose a method to compute the reproduction number R _t , the transmission potential of an epidemic over time. R _t is estimated by minimizing a functional that enforces: 1) the ability to produce an incidence curve i _t corrected of the weekly periodic bias produced by the “weekend effect,” obtained from R _t through a renewal equation; and 2) the regularity of R _t . A good agreement is found between our R _t estimate and the one provided by the currently accepted method, EpiEstim, except that our method predicts R _t several days closer to present. We provide the mathematical arguments for this shift.

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.

The camera calibration parameters and the image processing chain which generated a given image are generally not available to the receiver. This happens for example with scanned photographs and for most JPEG images. These images have undergone various nonlinear contrast changes and also linear and nonlinear filters. To deal with remnant noise in such images, we introduce a general nonparametric intensity and frequency-dependent noise model. We demonstrate by simulated and experiments with real images that this model, which requires the estimation of more than 1000 parameters, performs an efficient noise estimation. The proposed noise model is a patch model. Its estimation can therefore be used as a preliminary step to any patch-based denoising method. Our noise estimation method introduces several new tools for performing this complex estimation. One of them is a new sparse patch distance function permitting to find noisy patches with similar underlying geometry. A validation of the noise model and of its estimation method is obtained by comparing its results to ground-truth noise curves for both raw and JPEG-encoded images, and by visual inspection of the denoising results of real images. A fair comparison with the state of the art is also performed.

This work describes a method for detecting JPEG compression as well as its grid origin. The JPEG algorithm performs a quantization of the DCT coefficients of non-overlapping 8 x 8 blocks of images, setting many of those coefficients to zero. The method described here exploits these facts and identifies the presence of a JPEG grid when a significant number of DCT zeros is observed for a given grid origin. This method can be applied globally to identify a JPEG compression, and also locally to identify image forgeries when misaligned or missing JPEG grids are found. The algorithm includes a statistical validation step according to Desolneux, Moisan and Morel's a contrario theory, which associates a number of false alarms (NFA) with each tampering detection. Detections are obtained by a threshold of the NFA, which renders the method fully automatic and endows it with a false alarm control mechanism.

Images undergo a complex processing chain from the moment light reaches the camera's sensor until the final digital image is delivered. Each of its operations leaves traces on the noise model which enable forgery detection through noise analysis. In this article, we describe the Noisesniffer method [Gardella et al., Noisesniffer: a Fully Automatic Image Forgery Detector Based on Noise Analysis, IEEE International Workshop on Biometrics and Forensics, 2021]. This method estimates for each image a background stochastic model which makes it possible to detect local noise anomalies characterized by their number of false alarms. We improve on the original formulation of the method by introducing a region-growing algorithm to detect local deviations from the background model. Results show that the proposed method outperforms the previous version as well as the state of the art.