SISL:Self-Supervised Image Signature Learning for Splicing Detection &amp; Localization

Agrawal, Susmit; Kumar, Prabhat; Seth, Siddharth; Parag, Toufiq; Singh, Maneesh; Babu, Venkatesh

doi:10.1109/cvprw56347.2022.00012

Cited by 8 publications

(1 citation statement)

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“…The SVM classifier then employs these attributes to classify genuine and spliced pictures. In their proposed system, Agarwal et al [56] used a self-supervised method for training splicing detection/localization models using an image's frequency transform "real-valued fast Fourier transform" (RFFT) algorithm. The deep network developed a representation to capture an image-specific signature by enforcing (image) self-consistency to detect the spliced areas.…”

Section: Image Splicingmentioning

confidence: 99%

A Survey of Detection and Mitigation for Fake Images on Social Media Platforms

Sharma,

Singh,

Agarwal

et al. 2023

Applied Sciences

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Recently, the spread of fake images on social media platforms has become a significant concern for individuals, organizations, and governments. These images are often created using sophisticated techniques to spread misinformation, influence public opinion, and threaten national security. This paper begins by defining fake images and their potential impact on society, including the spread of misinformation and the erosion of trust in digital media. This paper also examines the different types of fake images and their challenges for detection. We then review the recent approaches proposed for detecting fake images, including digital forensics, machine learning, and deep learning. These approaches are evaluated in terms of their strengths and limitations, highlighting the need for further research. This paper also highlights the need for multimodal approaches that combine multiple sources of information, such as text, images, and videos. Furthermore, we present an overview of existing datasets, evaluation metrics, and benchmarking tools for fake image detection. This paper concludes by discussing future directions for fake image detection research, such as developing more robust and explainable methods, cross-modal fake detection, and the integration of social context. It also emphasizes the need for interdisciplinary research that combines computer science, digital forensics, and cognitive psychology experts to tackle the complex problem of fake images. This survey paper will be a valuable resource for researchers and practitioners working on fake image detection on social media platforms.

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Section: Image Splicingmentioning

confidence: 99%