Video manipulation detection plays a vital role in modern multimedia forensics. In particular, double compression detection provides significant clues leading to the video edition history and hinting at potential malevolent manipulation. While such an analysis is well-understood on images, the research on this subject remains lacking in videos and existing methods are not yet able to reliably detect double-compressed videos. This work presents a novel method for identifying double compression in H.264 codec videos. Our technique exploits the periodicity of frame residuals caused by fixed Group of Pictures in the initial compression, and employs an a contrario framework to minimize and control false detections. The proposed method can reliably detect double compression in videos. It does not require threshold tuning, thus enabling automatic detection. The code is available at https://github.com/li-yanhao/gop_detection.

We propose a block-based signal-dependent noise estimation method on videos, that leverages inter-frame redundancy to separate noise from signal. Block matching is applied to find block pairs between two consecutive frames with similar signal. Then Ponomarenko’s method is extended by sorting pairs by their low-frequency energy and estimating noise in the high frequencies. Experiments on three datasets show that this method improves on the state of the art.

International audience

We propose a new method to evaluate image forensics tools, that characterizes what image cues are being used by each detector. Our method enables effortless creation of an arbitrarily large dataset of carefully tampered images in which controlled detection cues are present. Starting with raw images, we alter aspects of the image formation pipeline inside a mask, while leaving the rest of the image intact. This does not change the image’s interpretation; we thus call such alterations "non-semantic", as they yield no semantic inconsistencies. This method avoids the painful and often biased creation of convincing semantics. All aspects of image formation (noise, CFA, compression pattern and quality, etc.) can vary independently in both the authentic and tampered parts of the image. Alteration of a specific cue enables precise evaluation of the many forgery detectors that rely on this cue, and of the sensitivity of more generic forensic tools to each specific trace of forgery, and can be used to guide the combination of different methods. Based on this methodology, we create a database and conduct an evaluation of the main state-of-the-art image forensics tools, where we characterize the performance of each method with respect to each detection cue. Check qbammey.github.io/trace for the database and code.

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) has been providing the longest consistent data record of passive L-band (1.4 GHz) observations for more than ten years. SMOS, as well as the NASA missions SMAP and Aquarius have demonstrated the interest of L-band observations for land, ocean and cryosphere studies. The continuity of L-band observations must be assured taking into account that the spatial resolution (~ 40 km) of SMOS and SMAP is too coarse for some applications. Disaggregation strategies can be implemented but using airborne data, we show that the quality of the downscaled data cannot match that of an instrument with higher native resolution. The goal of the SMOS-HR (High Resolution) mission is to ensure the continuity of L-band observations while increasing the native resolution to 10 km. SMOS-HR will carry an array of ~ 230 antennas to perform aperture synthesis. The antenna distribution has been optimized to reduce the aliasing in the reconstructed images and SMOS-HR will incorporate advanced on-board Radio Frequency Interferences (RFI) mitigation techniques.

The Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the very first time, systematic passive L-band (1420−1427 MHz) measurements from space with a spatial resolution of ~50 Km. This contribution presents preliminary results of studies conducted for a High Resolution (HR) follow-on mission. The SMOS-HR project is currently undergoing a Phase 0 study by the French space agency. The goal is to ensure continuity of L-band measurements while increasing the spatial resolution to ~10 Km without degrading the radiometric sensitivity and keeping the revisit time of 3 days unchanged.

The European Space Agency (ESA) Soil Moisture and Ocean Salinity (SMOS) satellite has provided, for the first time, systematic passive L-band (1.4 GHz) measurements from space. This new data set, with a spatial resolution of ~40 km, has allowed a number of outstanding results over land (soil moisture, vegetation properties, frozen soils, ...), ocean (salinity, meso-scale phenomena, river plumes, high winds, ...) and cryosphere. SMOS, together with the NASA missions SMAP and Aquarius, have demonstrated the interest of the continuity of L-band observations. However, higher spatial resolution (1-10 km) is needed for applications related to water resources management and food security, for instance. Over the ocean as well as in coastal areas, higher resolution will bring the possibility to study in detail meso-scale processes and salinity (and density) variations closer to the coast. Over ice, higher spatial resolution will allow to monitor melting events in the coastal regions of Antarctica, for instance. In order to ensure the continuity of Earth observations in the L-band, while improving the resolution of the current generation of radiometers, new mission concepts are needed. We present the SMOS-HR (High-Resolution) project, which is currently in Phase 0 at CNES (Centre National d'Etudes Spatiales).

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.

Arguably several thousands papers are dedicated to image denoising. Most papers assume a fixed noise model, mainly white Gaussian or Poissonian. This assumption is only valid for raw images. Yet, in most images handled by the public and even by scientists, the noise model is imperfectly known or unknown. End users only dispose the result of a complex image processing chain effectuated by uncontrolled hardware and software (and sometimes by chemical means). For such images, recent progress in noise estimation permits to estimate from a single image a noise model, which is simultaneously signal and frequency dependent. We propose here a multiscale denoising algorithm adapted to this broad noise model. This leads to a blind denoising algorithm which we demonstrate on real JPEG images and on scans of old photographs for which the formation model is unknown. The consistency of this algorithm is also verified on simulated distorted images. This algorithm is finally compared with the unique state of the art previous blind denoising method.

We present a non-parametric method estimating an intensity and frequency dependent noise from a single image. The noise model is estimated on image patches and can be used consequently in all patch-based denoising methods. The method applies to cases where no access is granted to the image noise model, in particular to scanned photographs and JPEG images. The general noise model and the method to evaluate it are validated by comparing the estimations with the corresponding ground-truth curves for raw and JPEG images. Denoising experiments on scanned photographs also support the efficiency of the estimation method.

Most papers on denoising methods assume a white Gaussian noise model. Yet in most images handled by the public or by scientific users, the noise model is unknown and is not white, because of the various processes applied to the image before it reaches the user: scanning, demosaicing, compression, de-convolution, etc. To cope with this wide ranging problem, we propose a blind multiscale denoising algorithm working for noise which is simultaneously signal and frequency dependent. On noisy images coming from diverse sources (JPEG, scans of old photographs, ...) we show perceptually convincing results. This algorithm is compared to the state-of-the-art and it is also validated on images with white noise.