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Found 21 papers, 10 papers with code
OTJR: Optimal Transport Meets Optimal Jacobian Regularization for Adversarial Robustness
First, our work carefully analyzes and characterizes these two schools of approaches, both theoretically and empirically, to demonstrate how each approach impacts the robust learning of a classifier.
Interpretations Cannot Be Trusted: Stealthy and Effective Adversarial Perturbations against Interpretable Deep Learning
We assess the effectiveness of proposed attacks against two deep learning model architectures coupled with four interpretation models that represent different categories of interpretation models.
CFL-Net: Image Forgery Localization Using Contrastive Learning
A key assumption in underlying forged region localization is that there remains a difference of feature distribution between untampered and manipulated regions in each forged image sample, irrespective of the forgery type.
Towards an Awareness of Time Series Anomaly Detection Models' Adversarial Vulnerability
To the best of our understanding, we demonstrate, for the first time, the vulnerabilities of anomaly detection systems against adversarial attacks.
Deepfake Detection for Facial Images with Facemasks
However, none of the suggested deepfakedetection methods assessed the performance of deepfakes withthe facemask during the pandemic crisis after the outbreak of theCovid-19.
KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition
However, the majority of the proposed methods do not consider the class imbalance issue, which is a major challenge in practice for developing deep face recognition models.
VFP290K: A Large-Scale Benchmark Dataset for Vision-based Fallen Person Detection
Accordingly, detection of these anomalous events is of paramount importance for a number of applications, including but not limited to CCTV surveillance, security, and health care.
Ranked #1 on
Anomaly Detection In Surveillance Videos
on VFP290K
DA-FDFtNet: Dual Attention Fake Detection Fine-tuning Network to Detect Various AI-Generated Fake Images
Our DA-FDFtNet integrates the pre-trained model with Fine-Tune Transformer, MBblockV3, and a channel attention module to improve the performance and robustness across different types of fake images.
Exploring the Asynchronous of the Frequency Spectra of GAN-generated Facial Images
The rapid progression of Generative Adversarial Networks (GANs) has raised a concern of their misuse for malicious purposes, especially in creating fake face images.
ADD: Frequency Attention and Multi-View based Knowledge Distillation to Detect Low-Quality Compressed Deepfake Images
In particular, we propose the Attention-based Deepfake detection Distiller (ADD), which consists of two novel distillations: 1) frequency attention distillation that effectively retrieves the removed high-frequency components in the student network, and 2) multi-view attention distillation that creates multiple attention vectors by slicing the teacher's and student's tensors under different views to transfer the teacher tensor's distribution to the student more efficiently.
Evaluating the Robustness of Time Series Anomaly and Intrusion Detection Methods against Adversarial Attacks
To the best of our knowledge, we are the first to demonstrate the vulnerabilities of anomaly and intrusion detection systems against adversarial attacks.
Evaluation of an Audio-Video Multimodal Deepfake Dataset using Unimodal and Multimodal Detectors
On the other hand, to develop a good deepfake detector that can cope with the recent advancements in deepfake generation, we need to have a detector that can detect deepfakes of multiple modalities, i. e., videos and audios.
FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset
We generate this dataset using the most popular deepfake generation methods.
CoReD: Generalizing Fake Media Detection with Continual Representation using Distillation
Over the last few decades, artificial intelligence research has made tremendous strides, but it still heavily relies on fixed datasets in stationary environments.
FReTAL: Generalizing Deepfake Detection using Knowledge Distillation and Representation Learning
We use FReTAL to perform domain adaptation tasks on new deepfake datasets while minimizing catastrophic forgetting.
TAR: Generalized Forensic Framework to Detect Deepfakes using Weakly Supervised Learning
This motivates us to develop a generalized model to detect different types of deepfakes.
One Detector to Rule Them All: Towards a General Deepfake Attack Detection Framework
Beyond detecting a single type of DF from benchmark deepfake datasets, we focus on developing a generalized approach to detect multiple types of DFs, including deepfakes from unknown generation methods such as DeepFake-in-the-Wild (DFW) videos.
Am I a Real or Fake Celebrity? Measuring Commercial Face Recognition Web APIs under Deepfake Impersonation Attack
Moreover, we propose practical defense strategies to mitigate DI attacks, reducing the attack success rates to as low as 0% and 0. 02% for targeted and non-targeted attacks, respectively.
A Convolutional LSTM based Residual Network for Deepfake Video Detection
Also, they do not take advantage of the temporal information of the video.
T-GD: Transferable GAN-generated Images Detection Framework
First, we train the teacher model on the source dataset and use it as a starting point for learning the target dataset.
FDFtNet: Facing Off Fake Images using Fake Detection Fine-tuning Network
Creating fake images and videos such as "Deepfake" has become much easier these days due to the advancement in Generative Adversarial Networks (GANs).
