首页/论文/2004.01929

arXiv 2004.019292020-10-09eess.IVcs.CV

Empirical Evaluation of PRNU Fingerprint Variation for Mismatched Imaging Pipelines

Indian Institute of Technology Gandhinagar（甘地纳格尔印度理工学院）；New York University（纽约大学）；AGH University of Science and Technology（AGH科技大学）

Sharad Joshi, Pawel Korus, Nitin Khanna, Nasir Memon

备注：: 6 pages and 3 pages supplemental file

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

英文摘要相关论文

英文摘要：

We assess the variability of PRNU-based camera fingerprints with mismatched imaging pipelines (e.g., different camera ISP or digital darkroom software). We show that camera fingerprints exhibit non-negligible variations in this setup, which may lead to unexpected degradation of detection statistics in real-world use-cases. We tested 13 different pipelines, including standard digital darkroom software and recent neural-networks. We observed that correlation between fingerprints from mismatched pipelines drops on average to 0.38 and the PCE detection statistic drops by over 40%. The degradation in error rates is the strongest for small patches commonly used in photo manipulation detection, and when neural networks are used for photo development. At a fixed 0.5% FPR setting, the TPR drops by 17 ppt (percentage points) for 128 px and 256 px patches.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

阅读原文

阅读 PDF ↗

arXiv 页面 ↗HTML 原文 ↗

本文内容

英文摘要相关论文

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

继续探索

相关论文

Testing Robustness of Camera Fingerprint (PRNU) Detectors

In the field of forensic imaging, it is important to be able to extract a 'camera fingerprint' from one or a small set of images known to have been taken by the same camera. Ideally, that fingerprint would be used to identify an individual source camera. Camera fingerprint is based on certain kind of random noise present in all image sensors that is due to manufacturing imperfections and thus unique and impossible to avoid. PRNU (Photo-Response Non-Uniformity) has become the most widely used method for SCI (Source Camera Identification). In this paper, we design a set of 'attacks' to a PRNU ba…

2021-02-18eess.IV

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

Manipal Institute of Technology, Manipal Academy of Higher Education（曼尼帕尔理工学院，曼尼帕尔高等教育学院）；Deakin University（迪肯大学）

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…

2021-11-03cs.CRcs.CVeess.IV

PRNU Based Source Camera Identification for Webcam and Smartphone Videos

atlanTTic research center for Telecommunication Technologies, University of Vigo（维戈大学 atlanTTic 电信技术研究中心）

This communication is about an application of image forensics where we use camera sensor fingerprints to identify source camera (SCI: Source Camera Identification) in webcam/smartphone videos. Sensor or camera fingerprints are based on computing the intrinsic noise that is always present in this kind of sensors due to manufacturing imperfections. This is an unavoidable characteristic that links each sensor with its noise pattern. PRNU (Photo Response Non-Uniformity) has become the default technique to compute a camera fingerprint. There are many applications nowadays dealing with PRNU patterns…

2022-01-27cs.CVeess.IV

A leak in PRNU based source identification. Questioning fingerprint uniqueness

University of Florence（佛罗伦萨大学）；PIN Scrl（PIN 斯卡尔公司）

Photo Response Non-Uniformity (PRNU) is considered the most effective trace for the image source attribution task. Its uniqueness ensures that the sensor pattern noises extracted from different cameras are strongly uncorrelated, even when they belong to the same camera model. However, with the advent of computational photography, most recent devices heavily process the acquired pixels, possibly introducing non-unique artifacts that may reduce PRNU noise's distinctiveness, especially when several exemplars of the same device model are involved in the analysis. Considering that PRNU is an image …

2021-04-12cs.CVeess.IV

A comparison study of CNN denoisers on PRNU extraction

Southwest University of Science and Technology（西南科技大学）；SUNY Binghamton（纽约州立大学宾汉姆顿分校）

Performance of the sensor-based camera identification (SCI) method heavily relies on the denoising filter in estimating Photo-Response Non-Uniformity (PRNU). Given various attempts on enhancing the quality of the extracted PRNU, it still suffers from unsatisfactory performance in low-resolution images and high computational demand. Leveraging the similarity of PRNU estimation and image denoising, we take advantage of the latest achievements of Convolutional Neural Network (CNN)-based denoisers for PRNU extraction. In this paper, a comparative evaluation of such CNN denoisers on SCI performance…

2021-12-06cs.CVcs.MMeess.IV

Combining PRNU and noiseprint for robust and efficient device source identification

University Federico II of Naples（那不勒斯费德里科二世大学）

PRNU-based image processing is a key asset in digital multimedia forensics. It allows for reliable device identification and effective detection and localization of image forgeries, in very general conditions. However, performance impairs significantly in challenging conditions involving low quality and quantity of data. These include working on compressed and cropped images, or estimating the camera PRNU pattern based on only a few images. To boost the performance of PRNU-based analyses in such conditions we propose to leverage the image noiseprint, a recently proposed camera-model fingerprin…

2020-01-17cs.CVeess.IV

更多相关论文收起更多论文

PRNU Based Source Camera Identification for Webcam Videos

This communication is about an application of image forensics where we use camera sensor fingerprints to identify source camera (SCI: Source Camera Identification) in webcam videos. Sensor or camera fingerprints are based on computing the intrinsic noise that is always present in this kind of sensors due to manufacturing imperfections. This is an unavoidable characteristic that links each sensor with its noise pattern. PRNU (Photo Response Non-Uniformity) has become the default technique to compute a camera fingerprint. There are many applications nowadays dealing with PRNU patterns for camera…

2021-07-05cs.CReess.IV

Mitigation of H.264 and H.265 Video Compression for Reliable PRNU Estimation

The photo-response non-uniformity (PRNU) is a distinctive image sensor characteristic, and an imaging device inadvertently introduces its sensor's PRNU into all media it captures. Therefore, the PRNU can be regarded as a camera fingerprint and used for source attribution. The imaging pipeline in a camera, however, involves various processing steps that are detrimental to PRNU estimation. In the context of photographic images, these challenges are successfully addressed and the method for estimating a sensor's PRNU pattern is well established. However, various additional challenges related to g…

2019-05-23eess.IV

On Addressing the Impact of ISO Speed upon PRNU and Forgery Detection

University of Warwick（华威大学）；Deakin University（迪肯大学）

Photo Response Non-Uniformity (PRNU) has been used as a powerful device fingerprint for image forgery detection because image forgeries can be revealed by finding the absence of the PRNU in the manipulated areas. The correlation between an image's noise residual with the device's reference PRNU is often compared with a decision threshold to check the existence of the PRNU. A PRNU correlation predictor is usually used to determine this decision threshold assuming the correlation is content-dependent. However, we found that not only the correlation is content-dependent, but it also depends on th…

2020-06-20cs.CVcs.MMeess.IV

PRNU-Based Source Device Attribution for YouTube Videos

Photo Response Non-Uniformity (PRNU) is a camera imaging sensor imperfection which has earned a great interest for source device attribution of digital videos. A majority of recent researches about PRNU-based source device attribution for digital videos do not take into consideration the effects of video compression on the PRNU noise in video frames, but rather consider video frames as isolated images of equal importance. As a result, these methods perform poorly on re-compressed or low bit-rate videos. This paper proposes a novel method for PRNU fingerprint estimation from video frames taking…

2019-04-01eess.IVeess.SP

PRNU Emphasis: a Generalization of the Multiplicative Model

The photoresponse non-uniformity (PRNU) is a camera-specific pattern, widely adopted to solve multimedia forensics problems such as device identification or forgery detection. The theoretical analysis of this fingerprint customarily relies on a multiplicative model for the denoising residuals. This setup assumes that the nonlinear mapping from the scene irradiance to the preprocessed luminance, that is, the composition of the Camera Response Function (CRF) with the optical and digital preprocessing pipelines, is a gamma correction. Yet, this assumption seldom holds in practice. In this letter,…

2022-02-24eess.IVeess.SP

Camera Fingerprint Extraction via Spatial Domain Averaged Frames

Photo Response Non-Uniformity (PRNU) based camera attribution is an effective method to determine the source camera of visual media (an image or a video). To apply this method, images or videos need to be obtained from a camera to create a "camera fingerprint" which then can be compared against the PRNU of the query media whose origin is under question. The fingerprint extraction process can be time-consuming when a large number of video frames or images have to be denoised. This may need to be done when the individual images have been subjected to high compression or other geometric processin…

2019-09-10cs.MMeess.IV

On the Information Leakage of Camera Fingerprint Estimates

Camera fingerprints based on sensor PhotoResponse Non-Uniformity (PRNU) have gained broad popularity in forensic applications due to their ability to univocally identify the camera that captured a certain image. This fingerprint of a given sensor is extracted through some estimation method that requires a few images known to be taken with such sensor. In this paper, we show that the fingerprints extracted in this way leak a considerable amount of information from those images used in the estimation, thus constituting a potential threat to privacy. We propose to quantify the leakage via two mea…

2020-02-25eess.IV

Determining Image Sensor Temperature Using Dark Current

The state of the art method for fingerprinting digital cameras focuses on the non-uniform output of an array of photodiodes due to the distinct construction of the PN junction when excited by photons. This photo-response non-uniformity (PRNU) noise has shown to be effective but ignores knowledge of image sensor output under equilibrium states without excitation (dark current). The dark current response (DSN) traditionally has been deemed unsuitable as a source of fingerprinting as it is unstable across multiple variables including exposure time and temperature. As such it is currently ignored …

2019-01-08eess.IV

数字视频中源摄像机的盲取证识别与检测

Source Camera Identification and Detection in Digital Videos through Blind Forensics

National Institute of Technology, Rourkela（鲁尔克拉国家理工学院）

针对数字视频源摄像机识别问题，提出基于特征提取、特征选择与分类的盲取证技术，利用机器学习确定视频声称的源是否为原始源，实验证明其优于传统指纹方法。

2023-09-06cs.CVcs.LGeess.IV