Search Results for author: Huy H. Nguyen

Found 29 papers, 5 papers with code

Exploring Self-Supervised Vision Transformers for Deepfake Detection: A Comparative Analysis

no code implementations1 May 2024 Huy H. Nguyen, Junichi Yamagishi, Isao Echizen

This paper investigates the effectiveness of self-supervised pre-trained transformers compared to supervised pre-trained transformers and conventional neural networks (ConvNets) for detecting various types of deepfakes.

DeepFake Detection Face Swapping +3

Fine-Tuning Text-To-Image Diffusion Models for Class-Wise Spurious Feature Generation

no code implementations13 Feb 2024 AprilPyone MaungMaung, Huy H. Nguyen, Hitoshi Kiya, Isao Echizen

To this end, we utilize an existing approach of personalizing large-scale text-to-image diffusion models with available discovered spurious images and propose a new spurious feature similarity loss based on neural features of an adversarially robust model.

Image-Text Out-Of-Context Detection Using Synthetic Multimodal Misinformation

no code implementations29 Jan 2024 Fatma Shalabi, Huy H. Nguyen, Hichem Felouat, Ching-Chun Chang, Isao Echizen

Misinformation has become a major challenge in the era of increasing digital information, requiring the development of effective detection methods.

Misinformation Synthetic Data Generation

Leveraging Chat-Based Large Vision Language Models for Multimodal Out-Of-Context Detection

no code implementations22 Jan 2024 Fatma Shalabi, Hichem Felouat, Huy H. Nguyen, Isao Echizen

In this paper, we investigate the ability of LVLMs to detect multimodal OOC and show that these models cannot achieve high accuracy on OOC detection tasks without fine-tuning.

Image Classification Text Generation

Enhancing Robustness of LLM-Synthetic Text Detectors for Academic Writing: A Comprehensive Analysis

no code implementations16 Jan 2024 Zhicheng Dou, Yuchen Guo, Ching-Chun Chang, Huy H. Nguyen, Isao Echizen

In this paper, we present a comprehensive analysis of the impact of prompts on the text generated by LLMs and highlight the potential lack of robustness in one of the current state-of-the-art GPT detectors.

Cross-Attention Watermarking of Large Language Models

1 code implementation12 Jan 2024 Folco Bertini Baldassini, Huy H. Nguyen, Ching-Chung Chang, Isao Echizen

A new approach to linguistic watermarking of language models is presented in which information is imperceptibly inserted into the output text while preserving its readability and original meaning.

Surface Normal Estimation with Transformers

no code implementations11 Jan 2024 Barry Shichen Hu, Siyun Liang, Johannes Paetzold, Huy H. Nguyen, Isao Echizen, Jiapeng Tang

To avoid these limitations, we first unify the design choices in previous works and then propose a simplified Transformer-based model to extract richer and more robust geometric features for the surface normal estimation task.

Surface Normal Estimation

Generalized Deepfakes Detection with Reconstructed-Blended Images and Multi-scale Feature Reconstruction Network

no code implementations13 Dec 2023 Yuyang Sun, Huy H. Nguyen, Chun-Shien Lu, Zhiyong Zhang, Lu Sun, Isao Echizen

The growing diversity of digital face manipulation techniques has led to an urgent need for a universal and robust detection technology to mitigate the risks posed by malicious forgeries.

Face Swapping

How Close are Other Computer Vision Tasks to Deepfake Detection?

no code implementations2 Oct 2023 Huy H. Nguyen, Junichi Yamagishi, Isao Echizen

In this paper, we challenge the conventional belief that supervised ImageNet-trained models have strong generalizability and are suitable for use as feature extractors in deepfake detection.

DeepFake Detection Face Recognition +1

Defending Against Physical Adversarial Patch Attacks on Infrared Human Detection

no code implementations27 Sep 2023 Lukas Strack, Futa Waseda, Huy H. Nguyen, Yinqiang Zheng, Isao Echizen

To address this problem, we are the first to investigate defense strategies against adversarial patch attacks on infrared detection, especially human detection.

Data Augmentation Human Detection

Face Forgery Detection Based on Facial Region Displacement Trajectory Series

no code implementations7 Dec 2022 Yuyang Sun, Zhiyong Zhang, Isao Echizen, Huy H. Nguyen, Changzhen Qiu, Lu Sun

We introduce a method for detecting manipulated videos that is based on the trajectory of the facial region displacement.

Graph Attention

Analysis of Master Vein Attacks on Finger Vein Recognition Systems

no code implementations18 Oct 2022 Huy H. Nguyen, Trung-Nghia Le, Junichi Yamagishi, Isao Echizen

The results raise the alarm about the robustness of such systems and suggest that master vein attacks should be considered an important security measure.

Finger Vein Recognition

Rethinking Adversarial Examples for Location Privacy Protection

no code implementations28 Jun 2022 Trung-Nghia Le, Ta Gu, Huy H. Nguyen, Isao Echizen

We have investigated a new application of adversarial examples, namely location privacy protection against landmark recognition systems.

Image Manipulation Landmark Recognition

Closer Look at the Transferability of Adversarial Examples: How They Fool Different Models Differently

no code implementations29 Dec 2021 Futa Waseda, Sosuke Nishikawa, Trung-Nghia Le, Huy H. Nguyen, Isao Echizen

Deep neural networks are vulnerable to adversarial examples (AEs), which have adversarial transferability: AEs generated for the source model can mislead another (target) model's predictions.

Master Face Attacks on Face Recognition Systems

no code implementations8 Sep 2021 Huy H. Nguyen, Sébastien Marcel, Junichi Yamagishi, Isao Echizen

Previous work has proven the existence of master faces, i. e., faces that match multiple enrolled templates in face recognition systems, and their existence extends the ability of presentation attacks.

Face Recognition

OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wild

no code implementations ICCV 2021 Trung-Nghia Le, Huy H. Nguyen, Junichi Yamagishi, Isao Echizen

To promote these new tasks, we have created the first large-scale dataset posing a high level of challenges that is designed with face-wise rich annotations explicitly for face forgery detection and segmentation, namely OpenForensics.

Face Detection Face Swapping +1

Fashion-Guided Adversarial Attack on Person Segmentation

1 code implementation17 Apr 2021 Marc Treu, Trung-Nghia Le, Huy H. Nguyen, Junichi Yamagishi, Isao Echizen

It generates adversarial textures learned from fashion style images and then overlays them on the clothing regions in the original image to make all persons in the image invisible to person segmentation networks.

Adversarial Attack Human Instance Segmentation +2

Generating Master Faces for Use in Performing Wolf Attacks on Face Recognition Systems

no code implementations15 Jun 2020 Huy H. Nguyen, Junichi Yamagishi, Isao Echizen, Sébastien Marcel

In this work, we demonstrated that wolf (generic) faces, which we call "master faces," can also compromise face recognition systems and that the master face concept can be generalized in some cases.

Face Recognition

Detecting and Correcting Adversarial Images Using Image Processing Operations

no code implementations11 Dec 2019 Huy H. Nguyen, Minoru Kuribayashi, Junichi Yamagishi, Isao Echizen

Deep neural networks (DNNs) have achieved excellent performance on several tasks and have been widely applied in both academia and industry.

BIG-bench Machine Learning Object Recognition

Security of Facial Forensics Models Against Adversarial Attacks

no code implementations2 Nov 2019 Rong Huang, Fuming Fang, Huy H. Nguyen, Junichi Yamagishi, Isao Echizen

We experimentally demonstrated the existence of individual adversarial perturbations (IAPs) and universal adversarial perturbations (UAPs) that can lead a well-performed FFM to misbehave.

A Method for Identifying Origin of Digital Images Using a Convolution Neural Network

no code implementations2 Nov 2019 Rong Huang, Fuming Fang, Huy H. Nguyen, Junichi Yamagishi, Isao Echizen

The rapid development of deep learning techniques has created new challenges in identifying the origin of digital images because generative adversarial networks and variational autoencoders can create plausible digital images whose contents are not present in natural scenes.

Use of a Capsule Network to Detect Fake Images and Videos

2 code implementations28 Oct 2019 Huy H. Nguyen, Junichi Yamagishi, Isao Echizen

In this paper, we introduce a capsule network that can detect various kinds of attacks, from presentation attacks using printed images and replayed videos to attacks using fake videos created using deep learning.

Image and Video Forgery Detection

Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection

no code implementations22 Jul 2019 David Ifeoluwa Adelani, Haotian Mai, Fuming Fang, Huy H. Nguyen, Junichi Yamagishi, Isao Echizen

Advanced neural language models (NLMs) are widely used in sequence generation tasks because they are able to produce fluent and meaningful sentences.

Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos

1 code implementation17 Jun 2019 Huy H. Nguyen, Fuming Fang, Junichi Yamagishi, Isao Echizen

The output of one branch of the decoder is used for segmenting the manipulated regions while that of the other branch is used for reconstructing the input, which helps improve overall performance.

Binary Classification Decoder +3

Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos

3 code implementations26 Oct 2018 Huy H. Nguyen, Junichi Yamagishi, Isao Echizen

Recent advances in media generation techniques have made it easier for attackers to create forged images and videos.

Image and Video Forgery Detection

Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detector

no code implementations12 Apr 2018 Huy H. Nguyen, Ngoc-Dung T. Tieu, Hoang-Quoc Nguyen-Son, Junichi Yamagishi, Isao Echizen

Making computer-generated (CG) images more difficult to detect is an interesting problem in computer graphics and security.

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