A survey on digital camera identification methods

Bernacki, Jarosław

doi:10.1016/j.fsidi.2020.300983

Cited by 24 publications

(18 citation statements)

References 37 publications

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“…The third contribution of our work investigated the possibility of exploiting sensor imperfections in the camera through which the images had been taken to create a watermark to be used on SN platforms, that is, user-unaware watermarking . The sensor pattern noise (SPN) due to manufacturing imperfections is considered a unique characteristic that can fingerprint a source camera, and has been shown to outperform other techniques [ 9 ]. Our contribution in this paper is to show how it can be used for images shared online.…”

Section: Proposed Approachmentioning

confidence: 99%

“…Using the PRNU-based method, which is one of the most robust methods for device identification [ 9 ], in this paper we show that it can be used to solve cross-platform authorship attribution and verification, which is not possible with conventional watermarking algorithms. In particular, the proposed approach shows that, in addition to the classical digital forensics context, the method can be used to tackle privacy and security issues on online SN platforms.…”

Section: Introductionmentioning

confidence: 99%

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Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?

2022

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In the last decade, Social Networks (SNs) have deeply changed many aspects of society, and one of the most widespread behaviours is the sharing of pictures. However, malicious users often exploit shared pictures to create fake profiles, leading to the growth of cybercrime. Thus, keeping in mind this scenario, authorship attribution and verification through image watermarking techniques are becoming more and more important. In this paper, we firstly investigate how thirteen of the most popular SNs treat uploaded pictures in order to identify a possible implementation of image watermarking techniques by respective SNs. Second, we test the robustness of several image watermarking algorithms on these thirteen SNs. Finally, we verify whether a method based on the Photo-Response Non-Uniformity (PRNU) technique, which is usually used in digital forensic or image forgery detection activities, can be successfully used as a watermarking approach for authorship attribution and verification of pictures on SNs. The proposed method is sufficiently robust, in spite of the fact that pictures are often downgraded during the process of uploading to the SNs. Moreover, in comparison to conventional watermarking methods the proposed method can successfully pass through different SNs, solving related problems such as profile linking and fake profile detection. The results of our analysis on a real dataset of 8400 pictures show that the proposed method is more effective than other watermarking techniques and can help to address serious questions about privacy and security on SNs. Moreover, the proposed method paves the way for the definition of multi-factor online authentication mechanisms based on robust digital features.

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Section: Proposed Approachmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?

2022

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“…The growth of CPU and GPU capabilities allows the application of advanced algorithms, which are more reliable in moving object detection [ 39 ] and identification [ 40 , 41 ]. The parallel enhancement of the resolution of image sensors and the advance in optics make it possible to detect and identify even small objects from far distances [ 10 , 26 , 39 , 42 ].…”

Section: Background and Related Workmentioning

confidence: 99%

A Runway Safety System Based on Vertically Oriented Stereovision

Gradolewski

Dziak²,

Kaniecki³

et al. 2021

Sensors

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In 2020, over 10,000 bird strikes were reported in the USA, with average repair costs exceeding $200 million annually, rising to $1.2 billion worldwide. These collisions of avifauna with airplanes pose a significant threat to human safety and wildlife. This article presents a system dedicated to monitoring the space over an airport and is used to localize and identify moving objects. The solution is a stereovision based real-time bird protection system, which uses IoT and distributed computing concepts together with advanced HMI to provide the setup’s flexibility and usability. To create a high degree of customization, a modified stereovision system with freely oriented optical axes is proposed. To provide a market tailored solution affordable for small and medium size airports, a user-driven design methodology is used. The mathematical model is implemented and optimized in MATLAB. The implemented system prototype is verified in a real environment. The quantitative validation of the system performance is carried out using fixed-wing drones with GPS recorders. The results obtained prove the system’s high efficiency for detection and size classification in real-time, as well as a high degree of localization certainty.

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“…Without this information, our model could face suboptimal performance, as it would not be able to take advantage of the diversity introduced by the cross-camera triplets. To address this drawback, we aim to extend this work by incorporating techniques for automatic camera attribution [60], [61], allowing the identification of the camera used to acquire an image or identifying whether the same camera acquired a pair of images.…”

Section: Impact Of Ensemble-based Predictionmentioning

confidence: 99%

Unsupervised and self-adaptative techniques for cross-domain person re-identification

Bertocco¹,

Andaló²,

Rocha³

2021

Preprint

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Person Re-Identification (ReID) across nonoverlapping cameras is a challenging task and most works in the prior art rely on supervised feature learning from a labeled dataset to match the same person in different views. However, it demands the time-consuming task of labeling the acquired data, prohibiting its fast deployment in forensic scenarios. Unsupervised Domain Adaptation (UDA) emerges as a promising alternative, as it performs feature adaptation from a model trained on a source to a target domain without identitylabel annotation. However, most UDA-based methods rely upon a complex loss function with several hyper-parameters, which hinders the generalization to different scenarios. Moreover, as UDA depends on the translation between domains, it is important to select the most reliable data from the unseen domain, avoiding error propagation caused by noisy examples on the target data -an often overlooked problem. In this sense, we propose a novel UDA-based ReID method that optimizes a simple loss function with only one hyper-parameter and takes advantage of triplets of samples created by a new offline strategy based on the diversity of cameras within a cluster. This new strategy adapts and regularizes the model, avoiding overfitting on the target domain. We also introduce a new self-ensembling strategy, in which weights from different iterations are aggregated to create a final model combining knowledge from distinct moments of the adaptation. For evaluation, we consider three well-known deep learning architectures and combine them for final decision-making. The proposed method does not use person re-ranking nor any label on the target domain, and outperforms the state of the art, with a much simpler setup, on the Market to Duke, the challenging Market1501 to MSMT17, and Duke to MSMT17 adaptation scenarios.

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A survey on digital camera identification methods

Cited by 24 publications

References 37 publications

Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?

Are Social Networks Watermarking Us or Are We (Unawarely) Watermarking Ourself?

A Runway Safety System Based on Vertically Oriented Stereovision

Unsupervised and self-adaptative techniques for cross-domain person re-identification

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