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Found 13 papers, 5 papers with code
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.
Transcending Forgery Specificity with Latent Space Augmentation for Generalizable Deepfake Detection
Deepfake detection faces a critical generalization hurdle, with performance deteriorating when there is a mismatch between the distributions of training and testing data.
Not Just Learning from Others but Relying on Yourself: A New Perspective on Few-Shot Segmentation in Remote Sensing
In addition, to prevent the co-existence of multiple classes in remote sensing scenes from exacerbating the collapse of FSS generalization, we also propose a new Known-class Meta Suppressor (KMS) module to suppress the activation of known-class objects in the sample.
DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection
To fill this gap, we present the first comprehensive benchmark for deepfake detection, called DeepfakeBench, which offers three key contributions: 1) a unified data management system to ensure consistent input across all detectors, 2) an integrated framework for state-of-the-art methods implementation, and 3) standardized evaluation metrics and protocols to promote transparency and reproducibility.
UCF: Uncovering Common Features for Generalizable Deepfake Detection
Deepfake detection remains a challenging task due to the difficulty of generalizing to new types of forgeries.
Addressing Variable Dependency in GNN-based SAT Solving
Boolean satisfiability problem (SAT) is fundamental to many applications.
Multimodal Graph Learning for Deepfake Detection
We evaluate the effectiveness of our method through extensive experiments on widely-used benchmarks and demonstrate that our method outperforms the state-of-the-art detectors in terms of generalization ability and robustness against unknown disturbances.
HelixADMET: a robust and endpoint extensible ADMET system incorporating self-supervised knowledge transfer
Additionally, the pre-trained model provided by H-ADMET can be fine-tuned to generate new and customised ADMET endpoints, meeting various demands of drug research and development requirements.
The Vehicle Trajectory Prediction Based on ResNet and EfficientNet Model
But, the depth, width of the network and image resolution are still important reasons that restrict the accuracy of the model and the prediction results.
FAIR1M: A Benchmark Dataset for Fine-grained Object Recognition in High-Resolution Remote Sensing Imagery
In this paper, we propose a novel benchmark dataset with more than 1 million instances and more than 15, 000 images for Fine-grAined object recognItion in high-Resolution remote sensing imagery which is named as FAIR1M.
Inference with Hybrid Bio-hardware Neural Networks
This paper studies whether a bio-plausible model of a in vitro living neural network can be used to perform machine learning tasks and achieve good inference accuracy.
A Supervised STDP-based Training Algorithm for Living Neural Networks
Neural networks have shown great potential in many applications like speech recognition, drug discovery, image classification, and object detection.
Modified Euclidean Algorithms for Decoding Reed-Solomon Codes
The extended Euclidean algorithm (EEA) for polynomial greatest common divisors is commonly used in solving the key equation in the decoding of Reed-Solomon (RS) codes, and more generally in BCH decoding.
Information Theory Information Theory
