Search Results for author:
Found 35 papers, 9 papers with code
Hiding Faces in Plain Sight: Defending DeepFakes by Disrupting Face Detection
The motivation stems from the reliance of most DeepFake methods on face detectors to automatically extract victim faces from videos for training or synthesis (testing).
Forensics Adapter: Adapting CLIP for Generalizable Face Forgery Detection
We describe the Forensics Adapter, an adapter network designed to transform CLIP into an effective and generalizable face forgery detector.
HRGR: Enhancing Image Manipulation Detection via Hierarchical Region-aware Graph Reasoning
Subsequently, we describe a structural-agnostic graph reasoning strategy tailored for our graph to enhance the representation of nodes.
MPT: A Large-scale Multi-Phytoplankton Tracking Benchmark
Phytoplankton are a crucial component of aquatic ecosystems, and effective monitoring of them can provide valuable insights into ocean environments and ecosystem changes.
DPL: Cross-quality DeepFake Detection via Dual Progressive Learning
These varying qualities diversify the pattern of forgery traces, significantly increasing the difficulty of DeepFake detection.
Fake It till You Make It: Curricular Dynamic Forgery Augmentations towards General Deepfake Detection
Previous studies in deepfake detection have shown promising results when testing face forgeries from the same dataset as the training.
Active Fake: DeepFake Camouflage
However, these methods mainly concentrate on capturing the blending inconsistency in DeepFake faces, raising a new security issue, termed Active Fake, emerges when individuals intentionally create blending inconsistency in their authentic videos to evade responsibility.
UWStereo: A Large Synthetic Dataset for Underwater Stereo Matching
To enable further advance in underwater stereo matching, we introduce a large synthetic dataset called UWStereo.
FastForensics: Efficient Two-Stream Design for Real-Time Image Manipulation Detection
In this paper, we describe an efficient two-stream architecture for real-time image manipulation detection.
PhyTracker: An Online Tracker for Phytoplankton
Phytoplankton, a crucial component of aquatic ecosystems, requires efficient monitoring to understand marine ecological processes and environmental conditions.
High-order Neighborhoods Know More: HyperGraph Learning Meets Source-free Unsupervised Domain Adaptation
The drawback of these methods includes: 1) the pair-wise relation is limited to exposing the underlying correlations of two more samples, hindering the exploration of the structural information embedded in the target domain; 2) the clustering process only relies on the semantic feature, while overlooking the critical effect of domain shift, i. e., the distribution differences between the source and target domains.
Texture, Shape and Order Matter: A New Transformer Design for Sequential DeepFake Detection
Our method features four major improvements: \ding{182} we describe a new texture-aware branch that effectively captures subtle manipulation traces with a Diversiform Pixel Difference Attention module.
FreqBlender: Enhancing DeepFake Detection by Blending Frequency Knowledge
Existing methods typically generate these faces by blending real or fake faces in spatial domain.
DomainForensics: Exposing Face Forgery across Domains via Bi-directional Adaptation
Specifically, our strategy considers both forward and backward adaptation, to transfer the forgery knowledge from the source domain to the target domain in forward adaptation and then reverse the adaptation from the target domain to the source domain in backward adaptation.
COMICS: End-to-end Bi-grained Contrastive Learning for Multi-face Forgery Detection
One straightforward way to address this issue is to simultaneous process multi-face by integrating face extraction and forgery detection in an end-to-end fashion by adapting advanced object detection architectures.
ForensicsForest Family: A Series of Multi-scale Hierarchical Cascade Forests for Detecting GAN-generated Faces
The proposed ForensicsForest family is composed of three variants, which are {\em ForensicsForest}, {\em Hybrid ForensicsForest} and {\em Divide-and-Conquer ForensicsForest} respectively.
FakeTracer: Catching Face-swap DeepFakes via Implanting Traces in Training
In light of these two traces, our method can effectively expose DeepFakes by identifying them.
Enhancing the Transferability via Feature-Momentum Adversarial Attack
However, the guidance map is fixed in existing methods, which can not consistently reflect the behavior of networks as the image is changed during iteration.
Imperceptible Adversarial Examples for Fake Image Detection
Fooling people with highly realistic fake images generated with Deepfake or GANs brings a great social disturbance to our society.
DFGC 2021: A DeepFake Game Competition
This competition provides a common platform for benchmarking the adversarial game between current state-of-the-art DeepFake creation and detection methods.
DeepFake-o-meter: An Open Platform for DeepFake Detection
In recent years, the advent of deep learning-based techniques and the significant reduction in the cost of computation resulted in the feasibility of creating realistic videos of human faces, commonly known as DeepFakes.
Landmark Breaker: Obstructing DeepFake By Disturbing Landmark Extraction
In this paper, we describe Landmark Breaker, the first dedicated method to disrupt facial landmark extraction, and apply it to the obstruction of the generation of DeepFake videos. Our motivation is that disrupting the facial landmark extraction can affect the alignment of input face so as to degrade the DeepFake quality.
LandmarkGAN: Synthesizing Faces from Landmarks
Face synthesis is an important problem in computer vision with many applications.
Exposing GAN-generated Faces Using Inconsistent Corneal Specular Highlights
We show that such artifacts exist widely in high-quality GAN synthesized faces and further describe an automatic method to extract and compare corneal specular highlights from two eyes.
Fast Portrait Segmentation with Highly Light-weight Network
In this paper, we describe a fast and light-weight portrait segmentation method based on a new highly light-weight backbone (HLB) architecture.
Celeb-DF: A Large-scale Challenging Dataset for DeepFake Forensics
AI-synthesized face-swapping videos, commonly known as DeepFakes, is an emerging problem threatening the trustworthiness of online information.
Hiding Faces in Plain Sight: Disrupting AI Face Synthesis with Adversarial Perturbations
Recent years have seen fast development in synthesizing realistic human faces using AI technologies.
Exposing GAN-synthesized Faces Using Landmark Locations
Generative adversary networks (GANs) have recently led to highly realistic image synthesis results.
De-identification without losing faces
In this work, we describe a new face de-identification method that can preserve essential facial attributes in the faces while concealing the identities.
Exposing DeepFake Videos By Detecting Face Warping Artifacts
Compared to previous methods which use a large amount of real and DeepFake generated images to train CNN classifier, our method does not need DeepFake generated images as negative training examples since we target the artifacts in affine face warping as the distinctive feature to distinguish real and fake images.
Exposing Deep Fakes Using Inconsistent Head Poses
In this paper, we propose a new method to expose AI-generated fake face images or videos (commonly known as the Deep Fakes).
Exploring the Vulnerability of Single Shot Module in Object Detectors via Imperceptible Background Patches
In this paper, we focus on exploring the vulnerability of the Single Shot Module (SSM) commonly used in recent object detectors, by adding small perturbations to patches in the background outside the object.
Robust Adversarial Perturbation on Deep Proposal-based Models
Adversarial noises are useful tools to probe the weakness of deep learning based computer vision algorithms.
In Ictu Oculi: Exposing AI Generated Fake Face Videos by Detecting Eye Blinking
The new developments in deep generative networks have significantly improve the quality and efficiency in generating realistically-looking fake face videos.
