Forensic Analysis of Synthetically Generated Western Blot Images

Mandelli, Sara; Cozzolino, Davide; Cannas, Edoardo Daniele; Cardenuto, João Phillipe; Moreira, Daniel de Carvalho; Bestagini, Paolo; Scheirer, Walter J.; Rocha, Anderson; Verdoliva, Luisa; Tubaro, Stefano; Delp, Edward J.

doi:10.1109/access.2022.3179116

Cited by 18 publications

(8 citation statements)

References 27 publications

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“…This result indicates that the real and fake score distributions are perfectly separated, but the separating threshold is different from 0. As argued in many works, e.g., [35], when testing conditions are very different with respect to those considered for training (and validation), using the same fixed threshold typically does not work and may cause a wrong decision, with the consequent drop in ACC. To realign the ACC to the AUC As in the splicing case, in this scenario as well the LDP-TOP-based detector can achieve superior performance with respect to the CNN-based method, that, being completely data driven, requires a larger number of training videos to get good discrimination capabilities.…”

Section: Resultsmentioning

confidence: 99%

Just Dance: Detection of human body reenactment fake videos

Alamayreh

Fascella

Mandelli

et al. 2022

Preprint

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In the last few years, research on the detection of AI-generated videos has focused exclusively on detecting facial manipulations known as deepfakes. Much less attention has been paid to the detection of artificial non-facial fake videos. In this paper, we address a new forensic task, namely, the detection of fake videos of human body reenactment. To this purpose, we consider videos generated by the “Everybody Dance Now” framework. To accomplish our task, we have constructed and released a novel dataset of fake videos of this kind, referred to as FakeDance dataset. Additionally, we propose two forgery detectors to study the detectability of FakeDance kind of videos. The first one exploits spatial-temporal clues of a given video by means of hand-crafted descriptors, whereas the second detector is an end-to-end detector based on Convolutional Neural Networks (CNNs) trained on purpose. Both detectors have their peculiarities and strengths, working well in different operative scenarios. We believe that our proposed dataset together with the two detectors will contribute to the research on the detection of non-facial fake videos generated by means of AI.

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Section: Resultsmentioning

confidence: 99%

Just Dance: Detection of human body reenactment fake videos

Alamayreh

Fascella

Mandelli

et al. 2022

Preprint

Self Cite

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“…It thus remains to further SILA extensions the addition of single-image inspection solutions supported by noise analysis 42 . Another recent technology from our repertoire yet to be added is the detection of synthetically generated images, such as synthetic western blots 43 , which might be used to forge the outcome of ungrounded experiments. Lastly, SILA does not directly read yet images stored in Tag Image File Format (TIFF) containers, a feature presently under development.…”

Section: Discussionmentioning

confidence: 99%

SILA: a system for scientific image analysis

Moreira

Cardenuto²,

Shao³

et al. 2022

Sci Rep

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A great deal of the images found in scientific publications are retouched, reused, or composed to enhance the quality of the presentation. In most instances, these edits are benign and help the reader better understand the material in a paper. However, some edits are instances of scientific misconduct and undermine the integrity of the presented research. Determining the legitimacy of edits made to scientific images is an open problem that no current technology can perform satisfactorily in a fully automated fashion. It thus remains up to human experts to inspect images as part of the peer-review process. Nonetheless, image analysis technologies promise to become helpful to experts to perform such an essential yet arduous task. Therefore, we introduce SILA, a system that makes image analysis tools available to reviewers and editors in a principled way. Further, SILA is the first human-in-the-loop end-to-end system that starts by processing article PDF files, performs image manipulation detection on the automatically extracted figures, and ends with image provenance graphs expressing the relationships between the images in question, to explain potential problems. To assess its efficacy, we introduce a dataset of scientific papers from around the globe containing annotated image manipulations and inadvertent reuse, which can serve as a benchmark for the problem at hand. Qualitative and quantitative results of the system are described using this dataset.

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“…Moreover, the backward process involves learning the denoising process, i.e., from a noise-corrupted image to a clear image. Currently, there are still very few studies carried out in the sense of using diffusion models to generate deepfake [142,143] and, in the same sense, few studies involving the detection of deepfake also generated by diffusion models.…”

Section: Opportunities and Future Challengesmentioning

confidence: 99%

A Review of Deep Learning-based Approaches for Deepfake Content Detection

Passos,

Jodas,

Costa

et al. 2023

Preprint

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Recent advancements in deep learning generative models have raised concerns as they can create highly convincing counterfeit images and videos. This poses a threat to people's integrity and can lead to social instability. To address this issue, there is a pressing need to develop new computational models that can efficiently detect forged content and alert users to potential image and video manipulations. This paper presents a comprehensive review of recent studies for deepfake content detection using deep learning-based approaches. We aim to broaden the state-of-the-art research by systematically reviewing the different categories of fake content detection. Furthermore, we report the advantages and drawbacks of the examined works and future directions towards the issues and shortcomings still unsolved on deepfake detection.

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Forensic Analysis of Synthetically Generated Western Blot Images

Cited by 18 publications

References 27 publications

Just Dance: Detection of human body reenactment fake videos

Just Dance: Detection of human body reenactment fake videos

SILA: a system for scientific image analysis

A Review of Deep Learning-based Approaches for Deepfake Content Detection

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