activity
20122021
most citedDetecting Deepfake-Forged Contents with Separable Convolutional Neural Network and Image Segmentation

13 citations · 27 across the 7 of their papers we have counts for

collaborators

10 papers

cs.CV20212 cited

Perceptual Indistinguishability-Net (PI-Net): Facial Image Obfuscation with Manipulable Semantics

Jia-Wei Chen, Li-Ju Chen, Chia-Mu Yu +1

With the growing use of camera devices, the industry has many image datasets that provide more opportunities for collaboration between the machine learning community and industry.…

cs.LG20211 cited

Non-Singular Adversarial Robustness of Neural Networks

Yu-Lin Tsai, Chia-Yi Hsu, Chia-Mu Yu +1

Adversarial robustness has become an emerging challenge for neural network owing to its over-sensitivity to small input perturbations. While being critical, we argue that solving t…

cs.DB20209 cited

Privacy in Data Service Composition

Mahmoud Barhamgi, Charith Perera, Chia-Mu Yu +3

In modern information systems different information features, about the same individual, are often collected and managed by autonomous data collection services that may have differ…

cs.CV201913 cited

Detecting Deepfake-Forged Contents with Separable Convolutional Neural Network and Image Segmentation

Chia-Mu Yu, Ching-Tang Chang, Yen-Wu Ti

Recent advances in AI technology have made the forgery of digital images and videos easier, and it has become significantly more difficult to identify such forgeries. These forgeri…

math.ST2019

Locally Differentially Private Minimum Finding

Kazuto Fukuchi, Chia-Mu Yu, Arashi Haishima +1

We investigate a problem of finding the minimum, in which each user has a real value and we want to estimate the minimum of these values under the local differential privacy constr…

cs.CV2018

On The Utility of Conditional Generation Based Mutual Information for Characterizing Adversarial Subspaces

Chia-Yi Hsu, Pei-Hsuan Lu, Pin-Yu Chen +1

Recent studies have found that deep learning systems are vulnerable to adversarial examples; e.g., visually unrecognizable adversarial images can easily be crafted to result in mis…