On robustness of camera identification algorithms

Abstract

In this paper we consider the problem of a privacy threat enabling tracing digital cameras by the analysis of pictures they produced. As thousands of images are processed at a mass scale, the threat may apply to most users of digital cameras. We consider a state-of-theart algorithm for digital camera identification proposed in Lucas et al. (IEEE Trans Inf Forensics Secur 1(2):205-214, 2006) and discuss strategies that can be used to bypass it, in order to make information about the camera unavailable. It turns out that many natural strategies like Gaussian blur, adding artificial noise or removing pixels' least significant bit from the image does not prevent the identification of a camera unless a huge loss of image details is suffered. On the other hand, we show a method to bypass the camera identification with a just marginally more complex, yet not intuitive, method namely cropping the image on the edges and resizing to the original size using Lanczos resampling.

Robustness of digital camera identification with convolutional neural networks

“…This paper is a continuation of research presented in [3] and in [2]. In [3] there have been proposed two algorithms called PSNR-CT and DEPECHE.…”

“…The DEPECHE algorithm is used for prevention of camera identification based on the analysis of image histogram. In [2] the robustness of camera identification in terms of Lukás et al [27] by analysing degraded images has been checked. The degradation techniques included noising, blurring, removing least significant bit; also an algorithm to bypass the identification of Lukás et al's algorithm has been proposed.…”

“…The degradation techniques included noising, blurring, removing least significant bit; also an algorithm to bypass the identification of Lukás et al's algorithm has been proposed. However, in [2] the impact of image degradation techniques to camera identification of other than Lukás et al's algorithm -for example convolutional neural networks was not examined. Therefore this is the motivation for this paper.…”

DIPPAS: A Deep Image Prior PRNU Anonymization Scheme

“…For the implementation of [13], we consider the parameter configurations achieving the best anonymization results, i.e., the strategies defined as (5) 1 in the original paper. Moreover, to show that simple denoising does not achieve good anonymization performances [10], we implement the well-known DnCNN denoiser [30] which represents a modern data-driven solution among image denoising strategies.…”

“…The second family of methods works by blindly modifying pixel values in order to make the underlying PRNU unrecognizable. For instance, [9] shows that multiple image denoising steps can help attenuating the PRNU, even though this may not be enough to completely hinder its traces from images [10]. Alternatively, [11] applies seam-carving to change pixel locations, and [12] exploits patch-match techniques to scramble pixel positions.…”