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.

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.GALILEO: How would it be if your Highness were now to observe these impossible as well as unnecessary stars through this telescope? THE MATHEMATICIAN: One might be tempted to reply that your telescope, showing something which cannot exist, may not be a very reliable telescope, eh?

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.