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Works171 from public data
- Symmetric Cross Entropy for Robust Learning With Noisy Labels1,212
The proposed Symmetric cross entropy Learning (SL) approach simultaneously addresses both the under learning and overfitting problem of CE in the presence of noisy labels, and empirically shows that SL outperforms state-of-the-art methods.
- Improving Adversarial Robustness Requires Revisiting Misclassified Examples876
This paper proposes a new defense algorithm called MART, which explicitly differentiates the misclassified and correctly classified examples during the training, and shows that MART and its variant could significantly improve the state-of-the-art adversarial robustness.
- Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks640
Refool is proposed, a new type of backdoor attack inspired by an important natural phenomenon: reflection to plant reflections as backdoor into a victim model and can attack state-of-the-art DNNs with high success rate, and is resistant to state of theart backdoor defenses.
- WildDeepfake609
WildDeepfake is a small dataset that can be used, in addition to existing datasets, to develop and test the effectiveness of deepfake detectors against real-world deepfakes, and proposes two (eg. 2D and 3D) Attention-based Deepfake Detection Networks (ADDNets) to leverage the attention masks on real/fake faces for improved detection.
- Privacy and Robustness in Federated Learning: Attacks and Defenses582
A comprehensive survey on privacy and robustness in FL over the past five years is conducted and a concise introduction to the concept of FL is provided and a unique taxonomy covering privacy attacks and defenses is provided.
- Understanding adversarial attacks on deep learning based medical image analysis systems567
It is found that medical DNN models can be more vulnerable to adversarial attacks compared to models for natural images, according to two different viewpoints, and surprisingly, medical adversarialattacks can be easily detected.
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- Iterative Learning with Open-set Noisy Labels361
A novel iterative learning framework for training CNNs on datasets with open-set noisy labels that detects noisy labels and learns deep discriminative features in an iterative fashion and designs a Siamese network to encourage clean labels and noisy labels to be dissimilar.
- Clean-Label Backdoor Attacks on Video Recognition Models335
This paper proposes the use of a universal adversarial trigger as the backdoor trigger to attack video recognition models, a situation where backdoor attacks are likely to be challenged by the above 4 strict conditions.
- Adversarial Camouflage: Hiding Physical-World Attacks With Natural Styles285
Experimental evaluation shows that, in both digital and physical-world scenarios, adversarial examples crafted by Adversarial Camouflage are well camouflaged and highly stealthy, while remaining effective in fooling state-of-the-art DNN image classifiers.
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- Black-box Adversarial Attacks on Video Recognition Models175
V-BAD is a promising new tool to evaluate and improve the robustness of video recognition models to black-box adversarial attacks, and can craft both untargeted and targeted attacks to fool two state-of-the-art deepVideo recognition models.
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- Revisiting Adversarial Robustness Distillation: Robust Soft Labels Make Student Better150
The effectiveness of the RSLAD approach over existing adversarial training and distillation methods in improving the robustness of small models against state-of-the-art attacks including the AutoAttack is empirically demonstrated.
- A Comprehensive Survey in LLM(-Agent) Full Stack Safety: Data, Training and Deployment139
This paper introduces the concept of "full-stack" safety to systematically consider safety issues throughout the entire process of LLM training, deployment, and eventual commercialization, and represents the first safety survey to encompass the entire lifecycle of LLMs.
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- Reconstructive Neuron Pruning for Backdoor Defense107
This paper proposes a novel defense called Reconstructive Neuron Pruning (RNP) to expose and prune backdoor neurons via an unlearning and then recovering process and shows that such an asymmetric process on only a few clean samples can effectively expose andPrune the backdoor neurons implanted by a wide range of attacks.
- Copy, Right? A Testing Framework for Copyright Protection of Deep Learning Models107
A novel testing framework for deep learning copyright protection: DEEPJUDGE quantitatively tests the similarities between two deep learning models: a victim model and a suspect model, which leverages a diverse set of testing metrics and efficient test case generation algorithms to produce a chain of supporting evidence to help determine whether a suspects model is a copy of the victim model.
- VeriFi: Towards Verifiable Federated Unlearning92
This work prompts the concept of verifiable federated unlearning and proposes VeriFi, a unified framework that allows systematic analysis of federated unlearning and quantification of its effect, with different combinations of various unlearning and verification methods.
- RobOT: Robustness-Oriented Testing for Deep Learning Systems87
Experiments on multiple benchmark datasets confirm the effectiveness and efficiency of RobOT in improving DL model robustness, with 67.02% increase on the adversarial robustness that is 50.65% higher than the state-of-the-art work DeepGini.
- White-box Multimodal Jailbreaks Against Large Vision-Language Models82
A universal attack strategy that jointly attacks both text and image modalities to exploit a broader spectrum of vulnerability within VLMs and can effectively jailbreak MiniGPT-4 with a 96% success rate, highlighting the fragility of VLMs and the exigency for new alignment strategies.
- Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety76
This survey provides a systematic review of current safety research on large models, covering Vision Foundation Models, Large Language Models, Vision-Language Pre-training models, Vision-Language Models, Diffusion Models, and large-model-powered Agents, and presents a comprehensive taxonomy of safety threats to these models.
Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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