SPN-CNN: Boosting Sensor-Based Source Camera Attribution With Deep Learning

Abstract

We explore means to advance source camera identification based on sensor noise in a data-driven framework. Our focus is on improving the sensor pattern noise (SPN) extraction from a single image at test time. Where existing works suppress nuisance content with denoising filters that are largely agnostic to the specific SPN signal of interest, we demonstrate that a deep learning approach can yield a more suitable extractor that leads to improved source attribution. A series of extensive experiments on various public datasets confirms the feasibility of our approach and its applicability to image manipulation localization and video source attribution. A critical discussion of potential pitfalls completes the text.

Media forensics on social media platforms: a survey

“…Furthermore, many points still need to be addressed in order to reliably analyze images and videos in the wild, such as the investigation of new kinds of fingerprints and distinctive characteristics: i.e., the PRNU, although very robust in no-sharing scenarios, it has not proven so reliable on shared data. In this context, data-driven approaches based on deep learning might empower more effective strategies for fingerprint extraction, as it has been recently explored in [29].…”

Image Source Identification Using Convolutional Neural Networks in IoT Environment

“…By applying it in image forensics, the accuracy and universality of picture recognition can be 2 Wireless Communications and Mobile Computing improved. Also, [20] used the DnCNN [21] network models, extracted higher-quality image noise fingerprints, and performed correlation calculations based on the device fingerprints estimated by the maximum likelihood estimation to update the model parameters for better feature learning. So far, due to the extensiveness and heterogeneity of data information on social network platforms and the difficulty of high computational complexity caused by large-scale datasets for camera source identification algorithms, it is of great significance to combine the traditional PRNU-based noise estimation with the deep learning-based noise estimation and apply it to camera source identification and network forensics.…”

“…This trend should be closer to 1 − , and the loss function for ρðx, yÞ the penalty for negative numbers is very large, so the loss becomes a number greater than 1. Different from the loss function, MSEðx, yÞ = ð1/nÞ∑ n i=1 ðx i − y i Þ 2 is proposed in [20]. The loss function based on cosine distance proposed in this paper measures the degree of similarity between image's noise fingerprint and camera's PRNU fingerprint in the direction, while the loss function in [20] can only measure the absolute difference in space between the two.…”

“…Wireless Communications and Mobile Computing experimental results show that compared with the loss function proposed in [20], the loss function can make the model converge faster, and the training result is more stable.…”

A Leak in PRNU Based Source Identification—Questioning Fingerprint Uniqueness

“…a more general approach was proposed to manage the case of cropped and resized images, and the peak-tocorrelation-energy (PCE) ratio was introduced as a more robust way to measure the peak value of the correlation [6], [7]. To further improve its effectiveness and efficiency, several filters and pre-processing steps have been designed [8], [9], [10], [11], [12], and, recently, also data-driven approaches have been proposed [13], [14]. PRNU compression techniques have been developed to enable very large scales operations [15], [16], previously impossible due to the size of the PRNU pattern and the matching operations' complexity.…”