Addressing Deepfake Issue in Selfie banking through camera based authentication
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Abstract (arXiv)
Fake images in selfie banking are increasingly becoming a threat. Previously, it was just Photoshop, but now deep learning technologies enable us to create highly realistic fake identities, which fraudsters exploit to bypass biometric systems such as facial recognition in online banking. This paper explores the use of an already established forensic recognition system, previously used for picture camera localization, in deepfake detection.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 16 references that are in the map. First posted on arXiv 27 Aug 2025.
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9 of 16 references.
References · 9
- 2019FaceForensics++: Learning to Detect Manipulated Facial Imagescited by 1,101
- 2020Leveraging Frequency Analysis for Deep Fake Image Recognitioncited by 504
- 2020Media Forensics and DeepFakes: an overviewcited by 209
- 2019FakeCatcher: Detection of Synthetic Portrait Videos using Biological Signalscited by 145
- 2020The DeepFake Detection Challenge (DFDC) Datasetcited by 132
- 2018Noiseprint: a CNN-based camera model fingerprintcited by 114
- 2018In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinkingcited by 54
- 2024Faster Than Lies: Real-time Deepfake Detection using Binary Neural Networkscited by 27
- 2014An Overview of Face Liveness Detectioncited by 5
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