Search Results for author:
Found 9 papers, 7 papers with code
Exposing Text-Image Inconsistency Using Diffusion Models
In the battle against widespread online misinformation, a growing problem is text-image inconsistency, where images are misleadingly paired with texts with different intent or meaning.
DeepFake-O-Meter v2.0: An Open Platform for DeepFake Detection
Furthermore, it serves as an evaluation and benchmarking platform for researchers in digital media forensics to compare the performance of multiple algorithms on the same input.
Can ChatGPT Detect DeepFakes? A Study of Using Multimodal Large Language Models for Media Forensics
DeepFakes, which refer to AI-generated media content, have become an increasing concern due to their use as a means for disinformation.
Preserving Fairness Generalization in Deepfake Detection
The existing method for addressing this problem is providing a fair loss function.
Improving Fairness in Deepfake Detection
Despite the development of effective deepfake detectors in recent years, recent studies have demonstrated that biases in the data used to train these detectors can lead to disparities in detection accuracy across different races and genders.
AutoSplice: A Text-prompt Manipulated Image Dataset for Media Forensics
To achieve this, we propose a new approach that leverages the DALL-E2 language-image model to automatically generate and splice masked regions guided by a text prompt.
GLFF: Global and Local Feature Fusion for AI-synthesized Image Detection
To address this issue, we propose a Global and Local Feature Fusion (GLFF) framework to learn rich and discriminative representations by combining multi-scale global features from the whole image with refined local features from informative patches for AI synthesized image detection.
Fusing Global and Local Features for Generalized AI-Synthesized Image Detection
Specifically, we design a two-branch model to combine global spatial information from the whole image and local informative features from multiple patches selected by a novel patch selection module.
Modified Diversity of Class Probability Estimation Co-training for Hyperspectral Image Classification
Due to the limited amount and imbalanced classes of labeled training data, the conventional supervised learning can not ensure the discrimination of the learned feature for hyperspectral image (HSI) classification.
