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Found 37 papers, 8 papers with code
Defending Against Physical Adversarial Patch Attacks on Infrared Human Detection
To address this problem, we are the first to investigate defense strategies against adversarial patch attacks on infrared detection, especially human detection.
Hindering Adversarial Attacks with Multiple Encrypted Patch Embeddings
In this paper, we propose a new key-based defense focusing on both efficiency and robustness.
VoteTRANS: Detecting Adversarial Text without Training by Voting on Hard Labels of Transformations
Specifically, VoteTRANS detects adversarial text by comparing the hard labels of input text and its transformation.
Secure and Privacy Preserving Proxy Biometrics Identities
These proxy biometrics can be generated from original ones only with the help of a user-specific key.
Face Forgery Detection Based on Facial Region Displacement Trajectory Series
We introduce a method for detecting manipulated videos that is based on the trajectory of the facial region displacement.
Analysis of Master Vein Attacks on Finger Vein Recognition Systems
The results raise the alarm about the robustness of such systems and suggest that master vein attacks should be considered an important security measure.
On the Adversarial Transferability of ConvMixer Models
Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs).
On the Transferability of Adversarial Examples between Encrypted Models
Deep neural networks (DNNs) are well known to be vulnerable to adversarial examples (AEs).
Rethinking Adversarial Examples for Location Privacy Protection
We have investigated a new application of adversarial examples, namely location privacy protection against landmark recognition systems.
EASE: Entity-Aware Contrastive Learning of Sentence Embedding
We present EASE, a novel method for learning sentence embeddings via contrastive learning between sentences and their related entities.
Robust Deepfake On Unrestricted Media: Generation And Detection
Recent advances in deep learning have led to substantial improvements in deepfake generation, resulting in fake media with a more realistic appearance.
On the predictability in reversible steganography
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
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.
GUNNEL: Guided Mixup Augmentation and Multi-View Fusion for Aquatic Animal Segmentation
The dataset is available at https://doi. org/10. 5281/zenodo. 8208877 .
Effectiveness of Detection-based and Regression-based Approaches for Estimating Mask-Wearing Ratio
However, there is still a lack of comprehensive research on both methodologies and datasets.
A Multilingual Bag-of-Entities Model for Zero-Shot Cross-Lingual Text Classification
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).
Master Face Attacks on Face Recognition Systems
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.
OpenForensics: Large-Scale Challenging Dataset For Multi-Face Forgery Detection And Segmentation In-The-Wild
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.
Deep Learning for Predictive Analytics in Reversible Steganography
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
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.
Viable Threat on News Reading: Generating Biased News Using Natural Language Models
Recent advancements in natural language generation has raised serious concerns.
Generating Master Faces for Use in Performing Wolf Attacks on Face Recognition Systems
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.
Detecting and Correcting Adversarial Images Using Image Processing Operations
Deep neural networks (DNNs) have achieved excellent performance on several tasks and have been widely applied in both academia and industry.
Security of Facial Forensics Models Against Adversarial Attacks
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
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
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.
Generating Sentiment-Preserving Fake Online Reviews Using Neural Language Models and Their Human- and Machine-based Detection
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
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.
Speaker Anonymization Using X-vector and Neural Waveform Models
One solution to mitigate these concerns involves the concealing of speaker identities before the sharing of speech data.
Identifying Computer-Translated Paragraphs using Coherence Features
We have developed a method for extracting the coherence features from a paragraph by matching similar words in its sentences.
Audiovisual speaker conversion: jointly and simultaneously transforming facial expression and acoustic characteristics
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.
Capsule-Forensics: Using Capsule Networks to Detect Forged Images and Videos
Recent advances in media generation techniques have made it easier for attackers to create forged images and videos.
MesoNet: a Compact Facial Video Forgery Detection Network
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.
Transformation on Computer-Generated Facial Image to Avoid Detection by Spoofing Detector
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
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.
Can we steal your vocal identity from the Internet?: Initial investigation of cloning Obama's voice using GAN, WaveNet and low-quality found data
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.
