214 citations · 248 across the 10 of their papers we have counts for
10 papers
SELFI: Selective Fusion of Identity for Generalizable Deepfake Detection
Younghun Kim, Minsuk Jang, Myung-Joon Kwon +2
Face identity provides a powerful signal for deepfake detection. Prior studies show that even when not explicitly modeled, classifiers often learn identity features implicitly. Thi…
FRIDAY: Mitigating Unintentional Facial Identity in Deepfake Detectors Guided by Facial Recognizers
Younhun Kim, Myung-Joon Kwon, Wonjun Lee +1
Previous Deepfake detection methods perform well within their training domains, but their effectiveness diminishes significantly with new synthesis techniques. Recent studies have…
SAFIRE: Segment Any Forged Image Region
Myung-Joon Kwon, Wonjun Lee, Seung-Hun Nam +2
Most techniques approach the problem of image forgery localization as a binary segmentation task, training neural networks to label original areas as 0 and forged areas as 1. In co…
Breaking Temporal Consistency: Generating Video Universal Adversarial Perturbations Using Image Models
Hee-Seon Kim, Minji Son, Minbeom Kim +2
As video analysis using deep learning models becomes more widespread, the vulnerability of such models to adversarial attacks is becoming a pressing concern. In particular, Univers…
Introducing Competition to Boost the Transferability of Targeted Adversarial Examples through Clean Feature Mixup
Junyoung Byun, Myung-Joon Kwon, Seungju Cho +2
Deep neural networks are widely known to be susceptible to adversarial examples, which can cause incorrect predictions through subtle input modifications. These adversarial example…
Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Input
Junyoung Byun, Seungju Cho, Myung-Joon Kwon +2
The transferability of adversarial examples allows the deception on black-box models, and transfer-based targeted attacks have attracted a lot of interest due to their practical ap…
Learning JPEG Compression Artifacts for Image Manipulation Detection and Localization
Myung-Joon Kwon, Seung-Hun Nam, In-Jae Yu +2
Detecting and localizing image manipulation are necessary to counter malicious use of image editing techniques. Accordingly, it is essential to distinguish between authentic and ta…
DHNet: Double MPEG-4 Compression Detection via Multiple DCT Histograms
Seung-Hun Nam, Wonhyuk Ahn, Myung-Joon Kwon +2
In this article, we aim to detect the double compression of MPEG-4, a universal video codec that is built into surveillance systems and shooting devices. Double compression is acco…
Frame-rate Up-conversion Detection Based on Convolutional Neural Network for Learning Spatiotemporal Features
Minseok Yoon, Seung-Hun Nam, In-Jae Yu +3
With the advance in user-friendly and powerful video editing tools, anyone can easily manipulate videos without leaving prominent visual traces. Frame-rate up-conversion (FRUC), a…
Deep Convolutional Neural Network for Identifying Seam-Carving Forgery
Seung-Hun Nam, Wonhyuk Ahn, In-Jae Yu +3
Seam carving is a representative content-aware image retargeting approach to adjust the size of an image while preserving its visually prominent content. To maintain visually impor…