1 citations · 3 across the 15 of their papers we have counts for
15 papers
Optimal Look-back Horizon for Time Series Forecasting in Federated Learning
Dahao Tang, Nan Yang, Yanli Li +3
Selecting an appropriate look-back horizon remains a fundamental challenge in time series forecasting (TSF), particularly in the federated learning scenarios where data is decentra…
ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability
Zhiyu Zhu, Jiayu Zhang, Zhibo Jin +2
Attribution algorithms are essential for enhancing the interpretability and trustworthiness of deep learning models by identifying key features driving model decisions. Existing fr…
Narrowing Information Bottleneck Theory for Multimodal Image-Text Representations Interpretability
Zhiyu Zhu, Zhibo Jin, Jiayu Zhang +4
The task of identifying multimodal image-text representations has garnered increasing attention, particularly with models such as CLIP (Contrastive Language-Image Pretraining), whi…
PAR-AdvGAN: Improving Adversarial Attack Capability with Progressive Auto-Regression AdvGAN
Jiayu Zhang, Zhiyu Zhu, Xinyi Wang +4
Deep neural networks have demonstrated remarkable performance across various domains. However, they are vulnerable to adversarial examples, which can lead to erroneous predictions.…
Attribution for Enhanced Explanation with Transferable Adversarial eXploration
Zhiyu Zhu, Jiayu Zhang, Zhibo Jin +3
The interpretability of deep neural networks is crucial for understanding model decisions in various applications, including computer vision. AttEXplore++, an advanced framework bu…
AI-Compass: A Comprehensive and Effective Multi-module Testing Tool for AI Systems
Zhiyu Zhu, Zhibo Jin, Hongsheng Hu +5
AI systems, in particular with deep learning techniques, have demonstrated superior performance for various real-world applications. Given the need for tailored optimization in spe…
Enhancing Transferability of Adversarial Attacks with GE-AdvGAN+: A Comprehensive Framework for Gradient Editing
Zhibo Jin, Jiayu Zhang, Zhiyu Zhu +4
Transferable adversarial attacks pose significant threats to deep neural networks, particularly in black-box scenarios where internal model information is inaccessible. Studying ad…
Leveraging Information Consistency in Frequency and Spatial Domain for Adversarial Attacks
Zhibo Jin, Jiayu Zhang, Zhiyu Zhu +3
Adversarial examples are a key method to exploit deep neural networks. Using gradient information, such examples can be generated in an efficient way without altering the victim mo…
Enhancing Model Interpretability with Local Attribution over Global Exploration
Zhiyu Zhu, Zhibo Jin, Jiayu Zhang +1
In the field of artificial intelligence, AI models are frequently described as `black boxes' due to the obscurity of their internal mechanisms. It has ignited research interest on…
Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack
Zhibo Jin, Jiayu Zhang, Zhiyu Zhu +4
In recent times, the swift evolution of adversarial attacks has captured widespread attention, particularly concerning their transferability and other performance attributes. These…
DMS: Addressing Information Loss with More Steps for Pragmatic Adversarial Attacks
Zhiyu Zhu, Jiayu Zhang, Xinyi Wang +2
Despite the exceptional performance of deep neural networks (DNNs) across different domains, they are vulnerable to adversarial samples, in particular for tasks related to computer…
Benchmarking Transferable Adversarial Attacks
Zhibo Jin, Jiayu Zhang, Zhiyu Zhu +1
The robustness of deep learning models against adversarial attacks remains a pivotal concern. This study presents, for the first time, an exhaustive review of the transferability a…
GE-AdvGAN: Improving the transferability of adversarial samples by gradient editing-based adversarial generative model
Zhiyu Zhu, Huaming Chen, Xinyi Wang +5
Adversarial generative models, such as Generative Adversarial Networks (GANs), are widely applied for generating various types of data, i.e., images, text, and audio. Accordingly,…
MFABA: A More Faithful and Accelerated Boundary-based Attribution Method for Deep Neural Networks
Zhiyu Zhu, Huaming Chen, Jiayu Zhang +5
To better understand the output of deep neural networks (DNN), attribution based methods have been an important approach for model interpretability, which assign a score for each i…
DANAA: Towards transferable attacks with double adversarial neuron attribution
Zhibo Jin, Zhiyu Zhu, Xinyi Wang +3
While deep neural networks have excellent results in many fields, they are susceptible to interference from attacking samples resulting in erroneous judgments. Feature-level attack…