Camera model identification based on the generalized noise model in natural images

International audienceThe goal of this paper is to design a statistical test for the camera model identification problem. The approach is based on the generalized noise model that is developed by following the image processing pipeline of the digital camera. More specifically, this model is given by starting from the heteroscedastic noise model that describes the linear relation between the expectation and variance of a RAW pixel and taking into account the non-linear effect of gamma correction.The generalized noise model characterizes more accurately a natural image in TIFF or JPEG format. The present paper is similar to our previous work that was proposed for camera model identification from RAW images based on the heteroscedastic noise model. The parameters that are specified in the generalized noise model are used as camera fingerprint to identify camera models. The camera model identification problem is cast in the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the Likelihood Ratio Test is presented and its statistical performances are theoretically established. In practice when the model parameters are unknown, two Generalized Likelihood Ratio Tests are designed to deal with this difficulty such that they can meet a prescribed false alarm probability while ensuring a high detection performance. Numerical results on simulated images and real natural JPEG images highlight the relevance of the proposed approac

Individual camera device identification from JPEG images

International audienceThe goal of this paper is to investigate the problem of source camera device identification for natural images in JPEG format. We propose an improved signal-dependent noise model describing the statistical distribution of pixels from a JPEG image. The noise model relies on the heteroscedastic noise parameters, that relates the variance of pixels’ noise with the expectation considered as unique fingerprints. It is also shown in the present paper that, non-linear response of pixels can be captured by characterizing the linear relation because those heteroscedastic parameters, which are used to identify source camera device. The identification problem is cast within the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the Likelihood Ratio Test (LRT) is presented and its performance is theoretically established. The statistical performance of LRT serves as an upper bound of the detection power. In a practical identification, when the nuisance parameters are unknown, two generalized LRTs based on estimation of those parameters are established. Numerical results on simulated data and real natural images highlight the relevance of our proposed approach. While those results show a first positive proof of concept of the method, it still requires to be extended for a relevant comparison with PRNU-based approaches that benefit from years of experience

A probabilistic data-driven model for planar pushing

This paper presents a data-driven approach to model planar pushing interaction to predict both the most likely outcome of a push and its expected variability. The learned models rely on a variation of Gaussian processes with input-dependent noise called Variational Heteroscedastic Gaussian processes (VHGP) that capture the mean and variance of a stochastic function. We show that we can learn accurate models that outperform analytical models after less than 100 samples and saturate in performance with less than 1000 samples. We validate the results against a collected dataset of repeated trajectories, and use the learned models to study questions such as the nature of the variability in pushing, and the validity of the quasi-static assumption.Comment: 8 pages, 11 figures, ICRA 201

Generalized signal-dependent noise model and parameter estimation for natural images

International audienceThe goal of this paper is to propose a generalized signal-dependent noise model that is more appropriate to describe a natural image acquired by a digital camera than the conventional Additive White Gaussian Noise model widely used in image processing.This non-linear noise model takes into account effects in the image acquisition pipeline of a digital camera. In this paper, an algorithm for estimation of noise model parameters from a single image is designed. Then the proposed noise model is applied with the Local Linear Minimum Mean Square Error filter to design an efficient image denoising method

Heteroscedastic Uncertainty for Robust Generative Latent Dynamics

Learning or identifying dynamics from a sequence of high-dimensional observations is a difficult challenge in many domains, including reinforcement learning and control. The problem has recently been studied from a generative perspective through latent dynamics: high-dimensional observations are embedded into a lower-dimensional space in which the dynamics can be learned. Despite some successes, latent dynamics models have not yet been applied to real-world robotic systems where learned representations must be robust to a variety of perceptual confounds and noise sources not seen during training. In this paper, we present a method to jointly learn a latent state representation and the associated dynamics that is amenable for long-term planning and closed-loop control under perceptually difficult conditions. As our main contribution, we describe how our representation is able to capture a notion of heteroscedastic or input-specific uncertainty at test time by detecting novel or out-of-distribution (OOD) inputs. We present results from prediction and control experiments on two image-based tasks: a simulated pendulum balancing task and a real-world robotic manipulator reaching task. We demonstrate that our model produces significantly more accurate predictions and exhibits improved control performance, compared to a model that assumes homoscedastic uncertainty only, in the presence of varying degrees of input degradation.Comment: In IEEE Robotics and Automation Letters (RA-L) and presented at the IEEE International Conference on Intelligent Robots and Systems (IROS'20), Las Vegas, USA, October 25-29, 202

Camera model identification based on DCT coefficient statistics

International audienceThe goal of this paper is to design a statistical test for the camera model identification problem from JPEG images. The approach relies on the camera fingerprint extracted in the Discrete Cosine Transform (DCT) domain based on the state-of-the-art model of DCT coefficients. The camera model identification problem is cast in the framework of hypothesis testing theory. In an ideal context where all model parameters are perfectly known, the Likelihood Ratio Test is presented and its performances are theoretically established. For a practical use, two Generalized Likelihood Ratio Tests are designed to deal with unknown model parameters such that they can meet a prescribed false alarm probability while ensuring a high detection performance. Numerical results on simulated and real JPEG images highlight the relevance of the proposed approach

On the propagation of camera sensor noise to displacement maps obtained by DIC - an experimental study

International audienceThis paper focuses on one of the metrological properties of DIC, namely displacement resolution. More specifically, the study aims to validate, in the environment of an experimental mechanics laboratory, a recent generalized theoretical prediction of displacement resolution. Indeed, usual predictive formulas available in the literature neither take into account sub-pixel displacement, nor have been validated in an experimental mechanics laboratory environment, nor are applicable to all types of DIC (Global as well as Local). Here, the formula used to account for sub-pixel displacements is first recalled, and an accurate model of the sensor noise is introduced. The hypotheses required for the elaboration of this prediction are clearly stated. The formula is then validated using experimental data. Since rigid body motion between the specimen and the camera impairs the experimental data, and since sensor noise is signal-dependent, particular tools need to be introduced in order to ensure the consistency between the observed image noise and the model on which prediction hypotheses are based. Pre-processing tools introduced for another full-field measurement approach, namely the Grid Method, are employed to address these issues