Camera Trace Erasing

On the Reliability of the PNU for Source Camera Identification Tasks

The PNU is an essential and reliable tool to perform SCI and, during the years, became a standard de-facto for this task in the forensic field. In this paper, we show that, although strategies exist that aim to cancel, modify, replace the PNU traces in a digital camera image, it is still possible, through our experimental method, to find residual traces of the noise produced by the sensor used to shoot the photo. Furthermore, we show that is possible to inject the PNU of a different camera in a target image and trace it back to the source camera, but only under the condition that the new camer…

SpoC: Spoofing Camera Fingerprints

Thanks to the fast progress in synthetic media generation, creating realistic false images has become very easy. Such images can be used to wrap "rich" fake news with enhanced credibility, spawning a new wave of high-impact, high-risk misinformation campaigns. Therefore, there is a fast-growing interest in reliable detectors of manipulated media. The most powerful detectors, to date, rely on the subtle traces left by any device on all images acquired by it. In particular, due to proprietary in-camera processes, like demosaicing or compression, each camera model leaves trademark traces that can…

Beyond PRNU: Learning Robust Device-Specific Fingerprint for Source Camera Identification

Source camera identification tools assist image forensic investigators to associate an image in question with a suspect camera. Various techniques have been developed based on the analysis of the subtle traces left in the images during the acquisition. The Photo Response Non Uniformity (PRNU) noise pattern caused by sensor imperfections has been proven to be an effective way to identify the source camera. The existing literature suggests that the PRNU is the only fingerprint that is device-specific and capable of identifying the exact source device. However, the PRNU is susceptible to camera s…

Forensic Similarity for Digital Images

In this paper we introduce a new digital image forensics approach called forensic similarity, which determines whether two image patches contain the same forensic trace or different forensic traces. One benefit of this approach is that prior knowledge, e.g. training samples, of a forensic trace are not required to make a forensic similarity decision on it in the future. To do this, we propose a two part deep-learning system composed of a CNN-based feature extractor and a three-layer neural network, called the similarity network. This system maps pairs of image patches to a score indicating whe…

扩散模型遇上图像反取证

Diffusion models meet image counter-forensics

本文研究利用扩散模型的净化能力擦除图像篡改痕迹以欺骗取证方法，实验表明其在欺骗效果和图像自然度上均优于现有反取证技术。

苹果的合成模糊噪声模式：特征分析与取证应用

Apple's Synthetic Defocus Noise Pattern: Characterization and Forensic Applications

本文研究了苹果设备人像模式图像中的合成模糊噪声模式，分析其特征并提出估计方法，探讨其在相机来源验证中的应用，提升取证准确性。