Search Results for author: Isao Echizen

Found 37 papers, 8 papers with code

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

Hindering Adversarial Attacks with Multiple Encrypted Patch Embeddings

no code implementations4 Sep 2023 AprilPyone MaungMaung, Isao Echizen, Hitoshi Kiya

In this paper, we propose a new key-based defense focusing on both efficiency and robustness.

Secure and Privacy Preserving Proxy Biometrics Identities

no code implementations21 Dec 2022 Harkeerat Kaur, Rishabh Shukla, Isao Echizen, Pritee Khanna

These proxy biometrics can be generated from original ones only with the help of a user-specific key.

Privacy Preserving

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

On the Adversarial Transferability of ConvMixer Models

no code implementations19 Sep 2022 Ryota Iijima, Miki Tanaka, Isao Echizen, Hitoshi Kiya

Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs).

Image Classification

On the Transferability of Adversarial Examples between Encrypted Models

no code implementations7 Sep 2022 Miki Tanaka, Isao Echizen, Hitoshi Kiya

Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs).

Image Classification

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

EASE: Entity-Aware Contrastive Learning of Sentence Embedding

1 code implementation NAACL 2022 Sosuke Nishikawa, Ryokan Ri, Ikuya Yamada, Yoshimasa Tsuruoka, Isao Echizen

We present EASE, a novel method for learning sentence embeddings via contrastive learning between sentences and their related entities.

Clustering Contrastive Learning +5

Robust Deepfake On Unrestricted Media: Generation And Detection

no code implementations13 Feb 2022 Trung-Nghia Le, Huy H Nguyen, Junichi Yamagishi, Isao Echizen

Recent advances in deep learning have led to substantial improvements in deepfake generation, resulting in fake media with a more realistic appearance.

DeepFake Detection Face Swapping

On the predictability in reversible steganography

no code implementations5 Feb 2022 Ching-Chun Chang, Xu Wang, Sisheng Chen, Hitoshi Kiya, Isao Echizen

The core strength of neural networks is the ability to render accurate predictions for a bewildering variety of data.

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.

A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification

no code implementations15 Oct 2021 Sosuke Nishikawa, Ikuya Yamada, Yoshimasa Tsuruoka, Isao Echizen

We present a multilingual bag-of-entities model that effectively boosts the performance of zero-shot cross-lingual text classification by extending a multilingual pre-trained language model (e. g., M-BERT).

Entity Typing Language Modelling +3

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

Deep Learning for Predictive Analytics in Reversible Steganography

no code implementations13 Jun 2021 Ching-Chun Chang, Xu Wang, Sisheng Chen, Isao Echizen, Victor Sanchez, Chang-Tsun Li

Given that reversibility is governed independently by the coding module, we narrow our focus to the incorporation of neural networks into the analytics module, which serves the purpose of predicting pixel intensities and a pivotal role in determining capacity and imperceptibility.

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 +1

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 Face Swapping +2

Audiovisual speaker conversion: jointly and simultaneously transforming facial expression and acoustic characteristics

no code implementations29 Oct 2018 Fuming Fang, Xin Wang, Junichi Yamagishi, Isao Echizen

Transforming the facial and acoustic features together makes it possible for the converted voice and facial expressions to be highly correlated and for the generated target speaker to appear and sound natural.

Image Reconstruction

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

MesoNet: a Compact Facial Video Forgery Detection Network

6 code implementations4 Sep 2018 Darius Afchar, Vincent Nozick, Junichi Yamagishi, Isao Echizen

This paper presents a method to automatically and efficiently detect face tampering in videos, and particularly focuses on two recent techniques used to generate hyper-realistic forged videos: Deepfake and Face2Face.

DeepFake Detection Face Swapping +2

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.

High-quality nonparallel voice conversion based on cycle-consistent adversarial network

no code implementations2 Apr 2018 Fuming Fang, Junichi Yamagishi, Isao Echizen, Jaime Lorenzo-Trueba

Although voice conversion (VC) algorithms have achieved remarkable success along with the development of machine learning, superior performance is still difficult to achieve when using nonparallel data.

Image-to-Image Translation Speech Synthesis +3

Can we steal your vocal identity from the Internet?: Initial investigation of cloning Obama's voice using GAN, WaveNet and low-quality found data

no code implementations2 Mar 2018 Jaime Lorenzo-Trueba, Fuming Fang, Xin Wang, Isao Echizen, Junichi Yamagishi, Tomi Kinnunen

Thanks to the growing availability of spoofing databases and rapid advances in using them, systems for detecting voice spoofing attacks are becoming more and more capable, and error rates close to zero are being reached for the ASVspoof2015 database.

Speech Enhancement Speech Synthesis +1

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