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Found 38 papers, 18 papers with code
Robust CLIP-Based Detector for Exposing Diffusion Model-Generated Images
Diffusion models (DMs) have revolutionized image generation, producing high-quality images with applications spanning various fields.
Benchmarking the Robustness of UAV Tracking Against Common Corruptions
Addressing this, we propose UAV-C, a large-scale benchmark for assessing robustness of UAV trackers under common corruptions.
Robust Light-Weight Facial Affective Behavior Recognition with CLIP
Human affective behavior analysis aims to delve into human expressions and behaviors to deepen our understanding of human emotions.
Robust COVID-19 Detection in CT Images with CLIP
In the realm of medical imaging, particularly for COVID-19 detection, deep learning models face substantial challenges such as the necessity for extensive computational resources, the paucity of well-annotated datasets, and a significant amount of unlabeled data.
Preserving Fairness Generalization in Deepfake Detection
The existing method for addressing this problem is providing a fair loss function.
Neural Radiance Fields in Medical Imaging: Challenges and Next Steps
Neural Radiance Fields (NeRF), as a pioneering technique in computer vision, offer great potential to revolutionize medical imaging by synthesizing three-dimensional representations from the projected two-dimensional image data.
Masked Conditional Diffusion Model for Enhancing Deepfake Detection
this paper present we put a new insight into diffusion model-based data augmentation, and propose a Masked Conditional Diffusion Model (MCDM) for enhancing deepfake detection.
Uncertainty-Aware Explainable Recommendation with Large Language Models
Providing explanations within the recommendation system would boost user satisfaction and foster trust, especially by elaborating on the reasons for selecting recommended items tailored to the user.
Detecting Multimedia Generated by Large AI Models: A Survey
The rapid advancement of Large AI Models (LAIMs), particularly diffusion models and large language models, has marked a new era where AI-generated multimedia is increasingly integrated into various aspects of daily life.
Efficient Image Super-Resolution via Symmetric Visual Attention Network
An important development direction in the Single-Image Super-Resolution (SISR) algorithms is to improve the efficiency of the algorithms.
Ranked #53 on
Image Super-Resolution
on Set14 - 4x upscaling
Synthesizing Black-box Anti-forensics DeepFakes with High Visual Quality
Besides, compared with the images processed by existing DeepFake anti-forensics methods, the visual qualities of anti-forensics DeepFakes rendered by the proposed method are significantly refined.
UMedNeRF: Uncertainty-aware Single View Volumetric Rendering for Medical Neural Radiance Fields
In the field of clinical medicine, computed tomography (CT) is an effective medical imaging modality for the diagnosis of various pathologies.
X-Transfer: A Transfer Learning-Based Framework for GAN-Generated Fake Image Detection
Generative adversarial networks (GANs) have remarkably advanced in diverse domains, especially image generation and editing.
Controlling Neural Style Transfer with Deep Reinforcement Learning
Controlling the degree of stylization in the Neural Style Transfer (NST) is a little tricky since it usually needs hand-engineering on hyper-parameters.
Improving Cross-dataset Deepfake Detection with Deep Information Decomposition
Deepfake technology poses a significant threat to security and social trust.
Image-to-Image Translation with Deep Reinforcement Learning
The key feature in the RL-I2IT framework is to decompose a monolithic learning process into small steps with a lightweight model to progressively transform a source image successively to a target image.
Outlier Robust Adversarial Training
Theoretically, we show that the learning objective of ORAT satisfies the $\mathcal{H}$-consistency in binary classification, which establishes it as a proper surrogate to adversarial 0/1 loss.
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.
Harnessing the Power of Text-image Contrastive Models for Automatic Detection of Online Misinformation
As growing usage of social media websites in the recent decades, the amount of news articles spreading online rapidly, resulting in an unprecedented scale of potentially fraudulent information.
Attacking Important Pixels for Anchor-free Detectors
Existing adversarial attacks on object detection focus on attacking anchor-based detectors, which may not work well for anchor-free detectors.
Distributionally Robust Survival Analysis: A Novel Fairness Loss Without Demographics
We propose a general approach for training survival analysis models that minimizes a worst-case error across all subpopulations that are large enough (occurring with at least a user-specified minimum probability).
RMBench: Benchmarking Deep Reinforcement Learning for Robotic Manipulator Control
Reinforcement learning is applied to solve actual complex tasks from high-dimensional, sensory inputs.
Rank-based Decomposable Losses in Machine Learning: A Survey
Following these categories, we review the literature on rank-based aggregate losses and rank-based individual losses.
Open-Eye: An Open Platform to Study Human Performance on Identifying AI-Synthesized Faces
In this work, we develop an online platform called Open-eye to study the human performance of AI-synthesized face detection.
PseudoProp: Robust Pseudo-Label Generation for Semi-Supervised Object Detection in Autonomous Driving Systems
Semi-supervised object detection methods are widely used in autonomous driving systems, where only a fraction of objects are labeled.
GAN-generated Faces Detection: A Survey and New Perspectives
Generative Adversarial Networks (GAN) have led to the generation of very realistic face images, which have been used in fake social media accounts and other disinformation matters that can generate profound impacts.
Differentially Private SGDA for Minimax Problems
We further provide its utility analysis in the nonconvex-strongly-concave setting which is the first-ever-known result in terms of the primal population risk.
Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration
We evaluate our method on several 2D and 3D medical image datasets, some of which contain large deformations.
Robust Attentive Deep Neural Network for Exposing GAN-generated Faces
However, images from existing public datasets do not represent real-world scenarios well enough in terms of view variations and data distributions (where real faces largely outnumber synthetic faces).
Eyes Tell All: Irregular Pupil Shapes Reveal GAN-generated Faces
Generative adversary network (GAN) generated high-realistic human faces have been used as profile images for fake social media accounts and are visually challenging to discern from real ones.
T$_k$ML-AP: Adversarial Attacks to Top-$k$ Multi-Label Learning
Top-$k$ multi-label learning, which returns the top-$k$ predicted labels from an input, has many practical applications such as image annotation, document analysis, and web search engine.
Sum of Ranked Range Loss for Supervised Learning
A combination loss of AoRR and TKML is proposed as a new learning objective for improving the robustness of multi-label learning in the face of outliers in sample and labels alike.
TkML-AP: Adversarial Attacks to Top-k Multi-Label Learning
Top-k multi-label learning, which returns the top-k predicted labels from an input, has many practical applications such as image annotation, document analysis, and web search engine.
Uncertainty Aware Semi-Supervised Learning on Graph Data
To clarify the reasons behind the results, we provided the theoretical proof that explains the relationships between different types of uncertainties considered in this work.
Learning by Minimizing the Sum of Ranked Range
In forming learning objectives, one oftentimes needs to aggregate a set of individual values to a single output.
Exposing GAN-generated Faces Using Inconsistent Corneal Specular Highlights
We show that such artifacts exist widely in high-quality GAN synthesized faces and further describe an automatic method to extract and compare corneal specular highlights from two eyes.
Uncertainty-Aware Prediction for Graph Neural Networks
In this work, we propose a Bayesian deep learning framework reflecting various types of uncertainties for classification predictions by leveraging the powerful modeling and learning capabilities of GNNs.
ParallelPC: an R package for efficient constraint based causal exploration
Discovering causal relationships from data is the ultimate goal of many research areas.
