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Found 13 papers, 6 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.
Why Do Deepfake Detectors Fail?
Recent rapid advancements in deepfake technology have allowed the creation of highly realistic fake media, such as video, image, and audio.
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.
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.
Integrative Tensor-based Anomaly Detection System For Satellites
Detecting anomalies is of growing importance for various industrial applications and mission-critical infrastructures, including satellite systems.
