Adversarial Analysis for Source Camera Identification

Wang, Bo; Zhao, Mengnan; Wang, Wei; Dai, Xiaorui; Li, Yang; Guo, Yanqing

doi:10.1109/tcsvt.2020.3047084

Cited by 8 publications

(2 citation statements)

References 35 publications

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“…For example, Xie et al [ 41 ] adopted a Generative Adversarial Nets-based anti-forensic framework that aims to erase operation-specific traces in order to improve the undetectability of generated images. Wang et al [ 42 ] proposed a powerful adversarial attack to deceive the source-camera identification task. Furthermore, current state-of-the-art CNN-based approaches [ 43 , 44 , 45 , 46 , 47 ] provide promising solutions to camera model identification.…”

Section: Introductionmentioning

confidence: 99%

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

Manisha

Lin

et al. 2022

Sensors

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Source-camera identification tools assist image forensics investigators to associate an image with a camera. 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. However, the PRNU is susceptible to camera settings, scene details, image processing operations (e.g., simple low-pass filtering or JPEG compression), and counter-forensic attacks. A forensic investigator unaware of malicious counter-forensic attacks or incidental image manipulation is at risk of being misled. The spatial synchronization requirement during the matching of two PRNUs also represents a major limitation of the PRNU. To address the PRNU’s fragility issue, in recent years, deep learning-based data-driven approaches have been developed to identify source-camera models. However, the source information learned by existing deep learning models is not able to distinguish individual cameras of the same model. In light of the vulnerabilities of the PRNU fingerprint and data-driven techniques, in this paper, we bring to light the existence of a new robust data-driven device-specific fingerprint in digital images that is capable of identifying individual cameras of the same model in practical forensic scenarios. We discover that the new device fingerprint is location-independent, stochastic, and globally available, which resolves the spatial synchronization issue. Unlike the PRNU, which resides in the high-frequency band, the new device fingerprint is extracted from the low- and mid-frequency bands, which resolves the fragility issue that the PRNU is unable to contend with. Our experiments on various datasets also demonstrate that the new fingerprint is highly resilient to image manipulations such as rotation, gamma correction, and aggressive JPEG compression.

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Section: Introductionmentioning

confidence: 99%

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

Manisha

Lin

et al. 2022

Sensors

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“…Furthermore, the fast advent of sensor technology and proprietary in-camera processing algorithms equipped with modern smart sensors have imposed increasing challenges to the community [ 33 , 34 ]. There have been some recent studies on SCI methods that are robust to adversarial attacks [ 35 , 36 ]. Although promising results have been reported with the increase of accumulated data, network complexity and training costs have increased dramatically.…”

Section: Introductionmentioning

confidence: 99%

Efficient Source Camera Identification with Diversity-Enhanced Patch Selection and Deep Residual Prediction

Liu

Zou

Yang

et al. 2021

Sensors

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Source camera identification has long been a hot topic in the field of image forensics. Besides conventional feature engineering algorithms developed based on studying the traces left upon shooting, several deep-learning-based methods have also emerged recently. However, identification performance is susceptible to image content and is far from satisfactory for small image patches in real demanding applications. In this paper, an efficient patch-level source camera identification method is proposed based on a convolutional neural network. First, in order to obtain improved robustness with reduced training cost, representative patches are selected according to multiple criteria for enhanced diversity in training data. Second, a fine-grained multiscale deep residual prediction module is proposed to reduce the impact of scene content. Finally, a modified VGG network is proposed for source camera identification at brand, model, and instance levels. A more critical patch-level evaluation protocol is also proposed for fair performance comparison. Abundant experimental results show that the proposed method achieves better results as compared with the state-of-the-art algorithms.

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