Search Results for author:
Found 28 papers, 5 papers with code
Fine-Tuning Text-To-Image Diffusion Models for Class-Wise Spurious Feature Generation
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
Misinformation has become a major challenge in the era of increasing digital information, requiring the development of effective detection methods.
Leveraging Chat-Based Large Vision Language Models for Multimodal Out-Of-Context Detection
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.
Enhancing Robustness of LLM-Synthetic Text Detectors for Academic Writing: A Comprehensive Analysis
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
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
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.
Generalized Deepfakes Detection with Reconstructed-Blended Images and Multi-scale Feature Reconstruction Network
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.
How Close are Other Computer Vision Tasks to Deepfake Detection?
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.
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.
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.
Rethinking Adversarial Examples for Location Privacy Protection
We have investigated a new application of adversarial examples, namely location privacy protection against landmark recognition systems.
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.
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.
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.
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.
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.
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.
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.
