No Matter What Images You Share, You Can Probably Be Fingerprinted Anyway

Rouhi, Rahimeh; Bertini, Flavio; Montesi, Danilo

doi:10.3390/jimaging7020033

Cited by 5 publications

(3 citation statements)

References 52 publications

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“…Several convolutional neural network architectures have been used to process images [ 30 , 31 , 32 ], including medical images, as in [ 8 , 18 , 33 , 34 ]. For this reason, this study investigated the performance of several convolutional neural network architectures for the classification of cervical cell nuclei obtained in Pap smears.…”

Section: Materials and Methodsmentioning

confidence: 99%

A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification

et al. 2021

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In recent years, deep learning methods have outperformed previous state-of-the-art machine learning techniques for several problems, including image classification. Classifying cells in Pap smear images is very challenging, and it is still of paramount importance for cytopathologists. The Pap test is a cervical cancer prevention test that tracks preneoplastic changes in cervical epithelial cells. Carrying out this exam is important in that early detection. It is directly related to a greater chance of curing or reducing the number of deaths caused by the disease. The analysis of Pap smears is exhaustive and repetitive, as it is performed manually by cytopathologists. Therefore, a tool that assists cytopathologists is needed. This work considers 10 deep convolutional neural networks and proposes an ensemble of the three best architectures to classify cervical cancer upon cell nuclei and reduce the professionals’ workload. The dataset used in the experiments is available in the Center for Recognition and Inspection of Cells (CRIC) Searchable Image Database. Considering the metrics of precision, recall, F1-score, accuracy, and sensitivity, the proposed ensemble improves previous methods shown in the literature for two- and three-class classification. We also introduce the six-class classification outcome.

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Section: Materials and Methodsmentioning

confidence: 99%

A Deep Learning Ensemble Method to Assist Cytopathologists in Pap Test Image Classification

et al. 2021

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“…Typically, these approaches fail if a malicious user falsifies the information stored in the fake profile, as usually happens. On the other hand, image-based approaches have recently provided promising results both without prior knowledge of the source camera [ 69 ] and in presence of outliers [ 46 ].…”

Section: Related Workmentioning

confidence: 99%

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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“…To this end, Maiano et al [13] presented a method for assessing the social media platform of provenance of a video sequence, considering the interrelation among features captured from videos as well as those shared by images. Rouhi et al [14] compared different classification-based methods to achieve both smartphone identification and user profile linking within social networks.…”

mentioning

confidence: 99%