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08/14/2023
On the Importance of Spatial Relations for Few-shot Action Recognition
Deep learning has achieved great success in video recognition, yet still...
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07/07/2023
Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs
Graph neural network (GNN) has gained increasing popularity in recent ye...
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05/24/2023
Reconstructive Neuron Pruning for Backdoor Defense
Deep neural networks (DNNs) have been found to be vulnerable to backdoor...
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05/04/2023
IMAP: Intrinsically Motivated Adversarial Policy
Reinforcement learning (RL) agents are known to be vulnerable to evasion...
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01/26/2023
Distilling Cognitive Backdoor Patterns within an Image
This paper proposes a simple method to distill and detect backdoor patte...
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12/31/2022
Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples
There is a growing interest in developing unlearnable examples (UEs) aga...
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11/28/2022
CIM: Constrained Intrinsic Motivation for Sparse-Reward Continuous Control
Intrinsic motivation is a promising exploration technique for solving re...
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11/15/2022
Backdoor Attacks on Time Series: A Generative Approach
Backdoor attacks have emerged as one of the major security threats to de...
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10/18/2022
Transferable Unlearnable Examples
With more people publishing their personal data online, unauthorized dat...
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10/18/2022
Fine-mixing: Mitigating Backdoors in Fine-tuned Language Models
Deep Neural Networks (DNNs) are known to be vulnerable to backdoor attac...
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07/12/2022
Backdoor Attacks on Crowd Counting
Crowd counting is a regression task that estimates the number of people ...
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05/25/2022
VeriFi: Towards Verifiable Federated Unlearning
Federated learning (FL) is a collaborative learning paradigm where parti...
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01/31/2022
Few-Shot Backdoor Attacks on Visual Object Tracking
Visual object tracking (VOT) has been widely adopted in mission-critical...
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12/15/2021
On the Convergence and Robustness of Adversarial Training
Improving the robustness of deep neural networks (DNNs) to adversarial e...
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12/10/2021
Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models
Deep learning (DL) models, especially those large-scale and high-perform...
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10/28/2021
SpineOne: A One-Stage Detection Framework for Degenerative Discs and Vertebrae
Spinal degeneration plagues many elders, office workers, and even the yo...
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10/26/2021
Alpha-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression
Bounding box (bbox) regression is a fundamental task in computer vision....
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10/22/2021
Anti-Backdoor Learning: Training Clean Models on Poisoned Data
Backdoor attack has emerged as a major security threat to deep neural ne...
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10/17/2021
ECG-ATK-GAN: Robustness against Adversarial Attacks on ECG using Conditional Generative Adversarial Networks
Recently deep learning has reached human-level performance in classifyin...
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10/07/2021
Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks
Deep neural networks (DNNs) are known to be vulnerable to adversarial at...
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08/18/2021
Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student Better
Adversarial training is one effective approach for training robust deep ...
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06/07/2021
Sub-trajectory Similarity Join with Obfuscation
User trajectory data is becoming increasingly accessible due to the prev...
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04/21/2021
Dual Head Adversarial Training
Deep neural networks (DNNs) are known to be vulnerable to adversarial ex...
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03/11/2021
Improving Adversarial Robustness via Channel-wise Activation Suppressing
The study of adversarial examples and their activation has attracted sig...
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02/16/2021
A Lazy Approach for Efficient Index Learning
Learned indices using neural networks have been shown to outperform trad...
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02/15/2021
Multi-class Classification Based Anomaly Detection of Insider Activities
Insider threats are the cyber attacks from within the trusted entities o...
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02/11/2021
RobOT: Robustness-Oriented Testing for Deep Learning Systems
Recently, there has been a significant growth of interest in applying so...
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01/18/2021
What Do Deep Nets Learn? Class-wise Patterns Revealed in the Input Space
Deep neural networks (DNNs) have been widely adopted in different applic...
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01/17/2021
Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions
Understanding the actions of both humans and artificial intelligence (AI...
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01/15/2021
Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks
Deep neural networks (DNNs) are known vulnerable to backdoor attacks, a ...
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01/13/2021
Unlearnable Examples: Making Personal Data Unexploitable
The volume of "free" data on the internet has been key to the current su...
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01/05/2021
WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection
In recent years, the abuse of a face swap technique called deepfake Deep...
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01/01/2021
Neural Architecture Search via Combinatorial Multi-Armed Bandit
Neural Architecture Search (NAS) has gained significant popularity as an...
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12/07/2020
Privacy and Robustness in Federated Learning: Attacks and Defenses
As data are increasingly being stored in different silos and societies b...
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07/18/2020
How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning
This paper firstly considers the research problem of fairness in collabo...
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07/05/2020
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
Recent studies have shown that DNNs can be compromised by backdoor attac...
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06/24/2020
Imbalanced Gradients: A New Cause of Overestimated Adversarial Robustness
Evaluating the robustness of a defense model is a challenging task in ad...
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06/24/2020
Normalized Loss Functions for Deep Learning with Noisy Labels
Robust loss functions are essential for training accurate deep neural ne...
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03/06/2020
Clean-Label Backdoor Attacks on Video Recognition Models
Deep neural networks (DNNs) are vulnerable to backdoor attacks which can...
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02/14/2020
Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets
Skip connections are an essential component of current state-of-the-art ...
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08/16/2019
Symmetric Cross Entropy for Robust Learning with Noisy Labels
Training accurate deep neural networks (DNNs) in the presence of noisy l...
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08/01/2019
Generative Image Inpainting with Submanifold Alignment
Image inpainting aims at restoring missing regions of corrupted images, ...
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07/24/2019
Understanding Adversarial Attacks on Deep Learning Based Medical Image Analysis Systems
Deep neural networks (DNNs) have become popular for medical image analys...
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06/04/2019
Towards Fair and Decentralized Privacy-Preserving Deep Learning with Blockchain
In collaborative deep learning, current learning frameworks follow eithe...
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05/02/2019
Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality
Generative Adversarial Networks (GANs) are an elegant mechanism for data...
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04/10/2019
Black-box Adversarial Attacks on Video Recognition Models
Deep neural networks (DNNs) are known for their vulnerability to adversa...
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07/22/2018
Learning Deep Hidden Nonlinear Dynamics from Aggregate Data
Learning nonlinear dynamics from diffusion data is a challenging problem...
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06/07/2018
Dimensionality-Driven Learning with Noisy Labels
Datasets with significant proportions of noisy (incorrect) class labels ...
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03/31/2018
Iterative Learning with Open-set Noisy Labels
Large-scale datasets possessing clean label annotations are crucial for ...
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01/08/2018
