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Found 8 papers, 2 papers with code
A3T: Accuracy Aware Adversarial Training
Adversarial training has been empirically shown to be more prone to overfitting than standard training.
GREENER: Graph Neural Networks for News Media Profiling
We study the problem of profiling news media on the Web with respect to their factuality of reporting and bias.
Impact of Adversarial Training on Robustness and Generalizability of Language Models
Specifically, we study the effect of pre-training data augmentation as well as training time input perturbations vs. embedding space perturbations on the robustness and generalization of transformer-based language models.
Ten Years after ImageNet: A 360° Perspective on AI
The rise of attention networks, self-supervised learning, generative modeling, and graph neural networks has widened the application space of AI.
A Survey on Predicting the Factuality and the Bias of News Media
The present level of proliferation of fake, biased, and propagandistic content online has made it impossible to fact-check every single suspicious claim or article, either manually or automatically.
Estimating Uniqueness of I-Vector Representation of Human Voice
The first custom dataset contains more than one and a half million speech samples of 20, 741 speakers obtained from TEDx Talks videos.
PRNU Estimation from Encoded Videos Using Block-Based Weighting
Estimating the photo-response non-uniformity (PRNU) of an imaging sensor from videos is a challenging task due to complications created by several processing steps in the camera imaging pipeline.
Image and Video Processing Cryptography and Security
Source Camera Verification from Strongly Stabilized Videos
Image stabilization performed during imaging and/or post-processing poses one of the most significant challenges to photo-response non-uniformity based source camera attribution from videos.
