Search Results for author:
Found 11 papers, 8 papers with code
Exposing the Deception: Uncovering More Forgery Clues for Deepfake Detection
In this paper, we try to tackle these challenges through three designs: (1) We present a novel framework to capture broader forgery clues by extracting multiple non-overlapping local representations and fusing them into a global semantic-rich feature.
FLTracer: Accurate Poisoning Attack Provenance in Federated Learning
In this paper, we first conduct a comprehensive study on prior FL attacks and detection methods.
SurrogatePrompt: Bypassing the Safety Filter of Text-To-Image Models via Substitution
Evaluation results disclose an 88% success rate in bypassing Midjourney's proprietary safety filter with our attack prompts, leading to the generation of counterfeit images depicting political figures in violent scenarios.
Locate and Verify: A Two-Stream Network for Improved Deepfake Detection
Deepfake has taken the world by storm, triggering a trust crisis.
DFIL: Deepfake Incremental Learning by Exploiting Domain-invariant Forgery Clues
However, the accuracy of detection models degrades significantly on images generated by new deepfake methods due to the difference in data distribution.
Text-CRS: A Generalized Certified Robustness Framework against Textual Adversarial Attacks
The language models, especially the basic text classification models, have been shown to be susceptible to textual adversarial attacks such as synonym substitution and word insertion attacks.
Is Imitation All You Need? Generalized Decision-Making with Dual-Phase Training
We introduce DualMind, a generalist agent designed to tackle various decision-making tasks that addresses challenges posed by current methods, such as overfitting behaviors and dependence on task-specific fine-tuning.
Masked Diffusion Models Are Fast Distribution Learners
In the pre-training stage, we propose to mask a high proportion (e. g., up to 90\%) of input images to approximately represent the primer distribution and introduce a masked denoising score matching objective to train a model to denoise visible areas.
Masked Autoencoders for Egocentric Video Understanding @ Ego4D Challenge 2022
In this report, we present our approach and empirical results of applying masked autoencoders in two egocentric video understanding tasks, namely, Object State Change Classification and PNR Temporal Localization, of Ego4D Challenge 2022.
Privacy-Utility Balanced Voice De-Identification Using Adversarial Examples
In this paper, we propose a voice de-identification system, which uses adversarial examples to balance the privacy and utility of voice services.
Learning-based Practical Smartphone Eavesdropping with Built-in Accelerometer
The second limitation comes from a common sense that these sensors can only pick up a narrow band (85-100Hz) of speech signals due to a sampling ceiling of 200Hz.
