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

Abstract

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.

Demosaicing to Detect Demosaicing and Image Forgeries

“…To make the detection truly automatic, a contrario analysis [14] can prove useful. It has already been successfully applied to other forgery detection methods [15]- [17]. Based on Gestalt theory, this detection paradigm computes automatic thresholds on the heat map by controlling an upper bound of the number of false alarms (NFA) one might expect.…”

Analysis and Experimentation on the ManTraNet Image Forgery Detector

“…for non-negative odd n (local scale) or n = −1 (global resolution), where σ F is the standard deviation of F , = 10 −5 and ω is a non-negative weight learned independently for each feature map. This process, known as Z-pooling, is performed at the global scale (n = −1) and at resolutions n ∈ [7,15,31]. The rationale for using local scales in addition to the global average is to mitigate the influence of multiple forgeries in an image.…”

“…Image forensics has become an important field of study over the past few years, sparked by the ubiquity of images on the internet and the proliferation of fake news in social media. Originally, image forgeries were mainly detected by manual methods targeting specific traces left by the image signal processing pipeline (ISP) such as demosaicing artifacts [3,10,13,14,20,23,27,33,35,4], JPEG compression [1,8,18,25,26,31,32,30], or noise inconsistencies [11,15,28,29].…”

A Contrario multi-scale anomaly detection method for industrial quality inspection

“…In this case, our normality assumption (H 0 ), is that pixels such that their distance to normality exceeds τ are uniformly distributed on the whole image. Following the idea in [34], we base this NFA criterion on the detection of high concentrations of these candidates as follows.…”