selected publications [full list]

1. PREPRINT Synthesizing Physical Backdoor Datasets: An Automated Framework Leveraging Deep Generative Models

   Sze Jue Yang, Chinh D La, Quang H Nguyen, Eugene Bagdasaryan, Kok-Seng Wong, Anh Tuan Tran, Chee Seng Chan, and Khoa D Doan

   2024

   Bib PDF

   @article{yang2023synthesizing,
     title = {Synthesizing Physical Backdoor Datasets: An Automated Framework Leveraging Deep Generative Models},
     author = {Yang, Sze Jue and La, Chinh D and Nguyen, Quang H and Bagdasaryan, Eugene and Wong, Kok-Seng and Tran, Anh Tuan and Chan, Chee Seng and Doan, Khoa D},
     year = {2024},
     bibtex_show = {true},
     selected = {true},
     abbr = {PREPRINT},
     pdf = {https://arxiv.org/abs/2312.03419}
   }

2. PREPRINT Forget-Me-Not: Making Backdoor Hard to be Forgotten in Fine-tuning

   Tran Ngoc Huynh, Anh Tran, Khoa D Doan, and Tung Pham

   2024

   Bib PDF

   @article{huynh2023fmn,
     title = {Forget-Me-Not: Making Backdoor Hard to be Forgotten in Fine-tuning},
     author = {Huynh, Tran Ngoc and Tran, Anh and Doan, Khoa D and Pham, Tung},
     year = {2024},
     bibtex_show = {true},
     abbr = {PREPRINT},
     pdf = {https://openreview.net/forum?id=T23HYw6lta},
     selected = {true}
   }

3. PREPRINT Fooling the Textual Fooler via Randomizing Latent Representations

   Cao-Duy Hoang, Quang H Nguyen, Saurav Manchanda, Minlong Peng, Kok-Seng Wong, and Khoa D Doan

   2024

   Bib PDF

   @article{hoang2023advfooler,
     title = {Fooling the Textual Fooler via Randomizing Latent Representations},
     author = {Hoang, Cao-Duy and Nguyen, Quang H and Manchanda, Saurav and Peng, Minlong and Wong, Kok-Seng and Doan, Khoa D},
     year = {2024},
     bibtex_show = {true},
     abbr = {PREPRINT},
     pdf = {https://arxiv.org/abs/2310.01452},
     selected = {true}
   }

4. ICLR Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks

   Quang H Nguyen, Yingjie Lao, Tung Pham, Kok-Seng Wong, and Khoa D Doan

   In The Twelfth International Conference on Learning Representations 2024

   Abs Bib PDF Submission History

   Recent works have shown that deep neural networks are vulnerable to adversarial examples that find samples close to the original image but can make the model misclassify. Even with access only to the model's output, an attacker can employ black-box attacks to generate such adversarial examples. In this work, we propose a simple and lightweight defense against black-box attacks by adding random noise to hidden features at intermediate layers of the model at inference time. Our theoretical analysis confirms that this method effectively enhances the model's resilience against both score-based and decision-based black-box attacks. Importantly, our defense does not necessitate adversarial training and has minimal impact on accuracy, rendering it applicable to any pre-trained model. Our analysis also reveals the significance of selectively adding noise to different parts of the model based on the gradient of the adversarial objective function, which can be varied during the attack. We demonstrate the robustness of our defense against multiple black-box attacks through extensive empirical experiments involving diverse models with various architectures.

   @inproceedings{nguyen2023randomized,
     title = {Understanding the Robustness of Randomized Feature Defense Against Query-Based Adversarial Attacks},
     author = {Nguyen, Quang H and Lao, Yingjie and Pham, Tung and Wong, Kok-Seng and Doan, Khoa D},
     booktitle = {The Twelfth International Conference on Learning Representations},
     year = {2024},
     bibtex_show = {true},
     abbr = {ICLR},
     pdf = {https://openreview.net/forum?id=vZ6r9GMT1n},
     selected = {true},
     submissions = {NeurIPS'23 -- ICLR'24}
   }

   NeurIPS'23 -- ICLR'24

5. NeurIPS Iba: Towards irreversible backdoor attacks in federated learning

   Thuy Dung Nguyen, Tuan A Nguyen, Anh Tran, Khoa D Doan, and Kok-Seng Wong

   Advances in Neural Information Processing Systems 2024

   Bib PDF Code Submission History

   @article{nguyen2024iba,
     title = {Iba: Towards irreversible backdoor attacks in federated learning},
     author = {Nguyen, Thuy Dung and Nguyen, Tuan A and Tran, Anh and Doan, Khoa D and Wong, Kok-Seng},
     journal = {Advances in Neural Information Processing Systems},
     volume = {36},
     year = {2024},
     submissions = {NeurIPS'23},
     bibtex_show = {true},
     abbr = {NeurIPS},
     pdf = {https://proceedings.neurips.cc/paper_files/paper/2023/hash/d0c6bc641a56bebee9d985b937307367-Abstract-Conference.html},
     code = {https://github.com/sail-research/iba},
     selected = {true}
   }

   NeurIPS'23

6. EAAI Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions

   Thuy Dung Nguyen, Tuan Nguyen, Phi Le Nguyen, Hieu H Pham, Khoa D Doan, and Kok-Seng Wong

   Engineering Applications of Artificial Intelligence 2024

   Bib PDF Submission History

   @article{nguyen2024backdoor,
     title = {Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions},
     author = {Nguyen, Thuy Dung and Nguyen, Tuan and Le Nguyen, Phi and Pham, Hieu H and Doan, Khoa D and Wong, Kok-Seng},
     journal = {Engineering Applications of Artificial Intelligence},
     volume = {127},
     pages = {107166},
     year = {2024},
     publisher = {Elsevier},
     submissions = {EAAI'24},
     bibtex_show = {true},
     abbr = {EAAI},
     pdf = {https://www.sciencedirect.com/science/article/pii/S0952197623013507},
     selected = {true}
   }

   EAAI'24

7. SIGIR Asymmetric Hashing for Fast Ranking via Neural Network Measures

   Khoa D Doan, Shulong Tan, Weijie Zhao, and Ping Li

   In 46th International ACM SIGIR Conference on Research and Development in Information Retrieval 2023

   Bib PDF Submission History

   @inproceedings{doan2023flora,
     bibtex_show = {true},
     abbr = {SIGIR},
     title = {Asymmetric Hashing for Fast Ranking via Neural Network Measures},
     author = {Doan, Khoa D and Tan, Shulong and Zhao, Weijie and Li, Ping},
     booktitle = {46th International ACM SIGIR Conference on Research and Development in Information Retrieval},
     year = {2023},
     submissions = {SIGIR'21 -- RecSys'21 -- WSDM'22 -- WWW'22 -- SIGIR'22 -- VLDB'23 -- CIKM'22 -- WWW'23 -- SIGIR'23},
     pdf = {https://dl.acm.org/doi/abs/10.1145/3539618.3591640},
     selected = {true}
   }

   SIGIR'21 -- RecSys'21 -- WSDM'22 -- WWW'22 -- SIGIR'22 -- VLDB'23 -- CIKM'22 -- WWW'23 -- SIGIR'23

8. AAAI Defending backdoor attacks on vision transformer via patch processing

   Khoa D Doan, Yingjie Lao, and Ping Li

   In AAAI Conference on Artificial Intelligence 2023

   Bib PDF Submission History

   @inproceedings{doan2023bdvitt,
     abbr = {AAAI},
     title = {Defending backdoor attacks on vision transformer via patch processing},
     author = {Doan, Khoa D and Lao, Yingjie and Li, Ping},
     booktitle = {AAAI Conference on Artificial Intelligence},
     year = {2023},
     submissions = {CVPR'22 -- ICCV'22 -- AAAI'23},
     bibtex_show = {true},
     selected = {true},
     teaser = {doan2023bdvits.png},
     pdf = {https://ojs.aaai.org/index.php/AAAI/article/view/25125}
   }

   CVPR'22 -- ICCV'22 -- AAAI'23

9. NeurIPS Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class

   Khoa D Doan, Yingjie Lao, and Ping Li

   In Thirty-Sixth Conference on Neural Information Processing Systems 2022

   Bib PDF Code Slides Submission History

   @inproceedings{doan2022marksman,
     bibtex_show = {true},
     abbr = {NeurIPS},
     title = {Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class},
     author = {Doan, Khoa D and Lao, Yingjie and Li, Ping},
     booktitle = {Thirty-Sixth Conference on Neural Information Processing Systems},
     year = {2022},
     submissions = {NeurIPS'22},
     teaser = {doan2022marksman.png},
     pdf = {https://openreview.net/forum?id=i-k6J4VkCDq},
     code = {https://github.com/khoadoan106/backdoor_attacks},
     slides = {https://nips.cc/media/neurips-2022/Slides/52924.pdf},
     selected = {true}
   }

   NeurIPS'22

10. CVPR One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching

    Khoa D Doan, Peng Yang, and Ping Li

    In Conference on Computer Vision and Pattern Recognition 2022

    Bib PDF Code Slides Submission History

    @inproceedings{doan2022hswd,
      bibtex_show = {true},
      abbr = {CVPR},
      title = {One Loss for Quantization: Deep Hashing with Discrete Wasserstein Distributional Matching},
      author = {Doan, Khoa D and Yang, Peng and Li, Ping},
      booktitle = {Conference on Computer Vision and Pattern Recognition},
      year = {2022},
      submissions = {CVPR'22},
      slides = {doan2022hswd-slides.pdf},
      code = {https://github.com/khoadoan106/single_loss_quantization},
      pdf = {https://openreview.net/pdf?id=uaqweIZ-9_k},
      teaser = {doan2022hswd.png},
      selected = {true}
    }

    CVPR'22

11. NeurIPS Backdoor Attack with Imperceptible Input and Latent Modification

    Khoa D Doan, Yingjie Lao, and Ping Li

    In Thirty-Fifth Conference on Neural Information Processing Systems 2021

    Bib PDF VIDEO Code Slides Submission History

    @inproceedings{doan2021wb,
      bibtex_show = {true},
      abbr = {NeurIPS},
      title = {Backdoor Attack with Imperceptible Input and Latent Modification},
      author = {Doan, Khoa D and Lao, Yingjie and Li, Ping},
      booktitle = {Thirty-Fifth Conference on Neural Information Processing Systems},
      year = {2021},
      submissions = {NeurIPS'21},
      pdf = {https://proceedings.neurips.cc/paper/2021/file/9d99197e2ebf03fc388d09f1e94af89b-Paper.pdf},
      code = {https://github.com/khoadoan106/backdoor_attacks},
      video = {https://recorder-v3.slideslive.com/?share=51522&s=8af881c0-56e8-451e-865f-adb1e90e5471},
      slides = {doan2021wb-slides.pdf},
      teaser = {doan2021wb.png},
      selected = {true}
    }

    NeurIPS'21

12. ICCV LIRA: Learnable, Imperceptible and Robust Backdoor Attacks

    Khoa D Doan, Yingjie Lao, Weijie Zhao, and Ping Li

    In International Conference on Computer Vision 2021

    Abs Bib PDF Code Poster Slides Submission History

    Recently, machine learning models have demonstrated to be vulnerable to backdoor attacks, primarily due to the lack of transparency in black-box models such as deep neural networks. A third-party model can be poisoned such that it works adequately in normal conditions but behaves maliciously on samples with specific trigger patterns. However, the trigger injection function is manually defined in most existing backdoor attack methods, e.g., placing a small patch of pixels on an image or slightly deforming the image before poisoning the model. This results in a two-stage approach with a sub-optimal attack success rate and a lack of complete stealthiness under human inspection. In this paper, we propose a novel and stealthy backdoor attack framework, LIRA, which jointly learns the optimal, stealthy trigger injection function and poisons the model. We formulate such an objective as a non-convex, constrained optimization problem. Under this optimization framework, the trigger generator function will learn to manipulate the input with imperceptible noise to preserve the model performance on the clean data and maximize the attack success rate on the poisoned data. Then, we solve this challenging optimization problem with an efficient, two-stage stochastic optimization procedure. Finally, the proposed attack framework achieves 100% success rates in several benchmark datasets, including MNIST, CIFAR10, GTSRB, and T-ImageNet, while simultaneously bypassing existing backdoor defense methods and human inspection.

    @inproceedings{doan2021lira,
      bibtex_show = {true},
      abbr = {ICCV},
      title = {LIRA: Learnable, Imperceptible and Robust Backdoor Attacks},
      author = {Doan, Khoa D and Lao, Yingjie and Zhao, Weijie and Li, Ping},
      booktitle = {International Conference on Computer Vision},
      submissions = {ICCV'21},
      code = {https://github.com/khoadoan106/backdoor_attacks},
      slides = {https://github.com/sunbelbd/invisible_backdoor_attacks/raw/master/resources/ICCV2021-LIRA-Slides.pdf},
      poster = {https://github.com/sunbelbd/invisible_backdoor_attacks/raw/master/resources/ICCV2021-LIRA-Poster.pdf},
      pdf = {https://openaccess.thecvf.com/content/ICCV2021/papers/Doan_LIRA_Learnable_Imperceptible_and_Robust_Backdoor_Attacks_ICCV_2021_paper.pdf},
      year = {2021},
      teaser = {doan2021lira.png},
      selected = {true}
    }

    ICCV'21

13. SIGIR Interpretable Graph Similarity Computation via Differentiable Optimal Alignment of Node Embeddings

    Khoa D Doan, Saurav Manchanda, Suchismit Mahapatra, and Chandan K Reddy

    In 44th International ACM SIGIR Conference on Research and Development in Information Retrieval 2021

    Abs Bib PDF VIDEO Code Slides Submission History

    Computing graph similarity is an important task in many graph-related applications such as retrieval in graph databases or graph clustering. While numerous measures have been proposed to capture the similarity between a pair of graphs, Graph Edit Distance (GED) and Maximum Common Subgraphs (MCS) are the two widely used measures in practice. GED and MCS are domain-agnostic measures of structural similarity between the graphs and define the similarity as a function of pairwise alignment of different entities (such as nodes, edges, and subgraphs) in the two graphs. The explicit explainability offered by the pairwise alignment provides transparency and justification of the similarity score, thus, GED and MCS have important practical applications. However, their exact computations are known to be NP-hard. While recently proposed neural-network based approximations have been shown to accurately compute these similarity scores, they have limited ability in providing comprehensive explanations compared to classical combinatorial algorithms, e.g., Beam search. This paper aims at efficiently approximating these domain-agnostic similarity measures through a neural network, and simultaneously learning the alignments (i.e., explanations) similar to those of classical intractable methods. Specifically, we formulate the similarity between a pair of graphs as the minimal "transformation" cost from one graph to another in the learnable node-embedding space. We show that, if node embedding is able to capture its neighborhood context closely, our proposed similarity function closely approximates both the alignment and the similarity score of classical methods. Furthermore, we also propose an efficient differentiable computation of our proposed objective for model training. Empirically, we demonstrate that the proposed method achieves up to 50%-100% reduction in the Mean Squared Error for the graph similarity approximation task and up to 20% improvement in the retrieval evaluation metrics for the graph retrieval task. The source code is available at https://github.com/khoadoan/GraphOTSim.

    @inproceedings{doan2021interpretable,
      bibtex_show = {true},
      abbr = {SIGIR},
      url = {https://doi.org/10.1145/3404835.3462960},
      code = {https://github.com/khoadoan/GraphOTSim},
      pdf = {doan2021interpretable.pdf},
      slides = {https://github.com/khoadoan/GraphOTSim/raw/main/resources/SIGIR21-fp0937-slides.pdf},
      video = {https://www.youtube.com/watch?v=IWxxsuFPsgs&t=1s},
      teaser = {doan2021interpretable.png},
      selected = {true},
      submissions = {AAAI'21 -- WWW'21 -- SIGIR'21},
      author = {Doan, Khoa D and Manchanda, Saurav and Mahapatra, Suchismit and Reddy, Chandan K},
      title = {Interpretable Graph Similarity Computation via Differentiable Optimal Alignment of Node Embeddings},
      year = {2021},
      isbn = {9781450380379},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      doi = {10.1145/3404835.3462960},
      booktitle = {44th International ACM SIGIR Conference on Research and Development in Information Retrieval},
      pages = {665–674},
      numpages = {10},
      keywords = {similarity search, model interpretability, graph similarity, GCN},
      location = {Virtual Event, Canada},
      series = {SIGIR '21}
    }

    AAAI'21 -- WWW'21 -- SIGIR'21

14. WWW Efficient Implicit Unsupervised Text Hashing Using Adversarial Autoencoder

    Khoa D Doan, and Chandan K Reddy

    In Proceedings of The Web Conference 2020

    Abs Bib HTML PDF

    Searching for documents with semantically similar content is a fundamental problem in the information retrieval domain with various challenges, primarily, in terms of efficiency and effectiveness. Despite the promise of modeling structured dependencies in documents, several existing text hashing methods lack an efficient mechanism to incorporate such vital information. Additionally, the desired characteristics of an ideal hash function, such as robustness to noise, low quantization error and bit balance/uncorrelation, are not effectively learned with existing methods. This is because of the requirement to either tune additional hyper-parameters or optimize these heuristically and explicitly constructed cost functions. In this paper, we propose a Denoising Adversarial Binary Autoencoder (DABA) model which presents a novel representation learning framework that captures structured representation of text documents in the learned hash function. Also, adversarial training provides an alternative direction to implicitly learn a hash function that captures all the desired characteristics of an ideal hash function. Essentially, DABA adopts a novel single-optimization adversarial training procedure that minimizes the Wasserstein distance in its primal domain to regularize the encoder’s output of either a recurrent neural network or a convolutional autoencoder. We empirically demonstrate the effectiveness of our proposed method in capturing the intrinsic semantic manifold of the related documents. The proposed method outperforms the current state-of-the-art shallow and deep unsupervised hashing methods for the document retrieval task on several prominent document collections.

    @inproceedings{doan2020efficient,
      bibtex_show = {true},
      abbr = {WWW},
      url = {https://doi.org/10.1145/3366423.3380150},
      html = {https://dl.acm.org/doi/abs/10.1145/3366423.3380150},
      pdf = {https://people.cs.vt.edu/~reddy/papers/WWW20a.pdf},
      teaser = {doan2020efficient.png},
      selected = {true},
      author = {Doan, Khoa D and Reddy, Chandan K},
      title = {Efficient Implicit Unsupervised Text Hashing Using Adversarial Autoencoder},
      year = {2020},
      isbn = {9781450370233},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      doi = {10.1145/3366423.3380150},
      booktitle = {Proceedings of The Web Conference},
      pages = {684–694},
      numpages = {11},
      keywords = {autoencoder, Hashing, adversarial training, deep learning.},
      location = {Taipei, Taiwan},
      series = {WWW '20}
    }

15. arXiv Gradient boosting neural networks: Grownet

    Sarkhan Badirli, Xuanqing Liu, Zhengming Xing, Avradeep Bhowmik, Khoa D Doan, and Sathiya K Selvaraj

    arXiv preprint arXiv:2002.07971 2020

    Abs Bib PDF Code

    A novel gradient boosting framework is proposed where shallow neural networks are employed as ``weak learners''. General loss functions are considered under this unified framework with specific examples presented for classification, regression, and learning to rank. A fully corrective step is incorporated to remedy the pitfall of greedy function approximation of classic gradient boosting decision tree. The proposed model rendered outperforming results against state-of-the-art boosting methods in all three tasks on multiple datasets. An ablation study is performed to shed light on the effect of each model components and model hyperparameters.

    @article{badirli2020gradient,
      bibtex_show = {true},
      abbr = {arXiv},
      url = {https://arxiv.org/abs/2002.07971},
      code = {https://github.com/sbadirli/GrowNet},
      pdf = {https://arxiv.org/pdf/2002.07971.pdf},
      teaser = {badirli2020gradient.png},
      selected = {true},
      title = {Gradient boosting neural networks: Grownet},
      author = {Badirli, Sarkhan and Liu, Xuanqing and Xing, Zhengming and Bhowmik, Avradeep and Doan, Khoa D and Selvaraj, Sathiya K},
      journal = {arXiv preprint arXiv:2002.07971},
      year = {2020}
    }

16. CIKM Adversarial Factorization Autoencoder for Look-Alike Modeling

    Khoa D Doan, Pranjul Yadav, and Chandan K Reddy

    In Proceedings of the 28th ACM International Conference on Information and Knowledge Management 2019

    Abs Bib PDF

    Digital advertising is performed in multiple ways, for e.g., contextual, display-based and search-based advertising. Across these avenues, the primary goal of the advertiser is to maximize the return on investment. To realize this, the advertiser often aims to target the advertisements towards a targeted set of audience as this set has a high likelihood to respond positively towards the advertisements. One such form of tailored and personalized, targeted advertising is known as look-alike modeling, where the advertiser provides a set of seed users and expects the machine learning model to identify a new set of users such that the newly identified set is similar to the seed-set with respect to the online purchasing activity. Existing look-alike modeling techniques (i.e., similarity-based and regression-based) suffer from serious limitations due to the implicit constraints induced during modeling. In addition, the high-dimensional and sparse nature of the advertising data increases the complexity. To overcome these limitations, in this paper, we propose a novel Adversarial Factorization Autoencoder that can efficiently learn a binary mapping from sparse, high-dimensional data to a binary address space through the use of an adversarial training procedure. We demonstrate the effectiveness of our proposed approach on a dataset obtained from a real-world setting and also systematically compare the performance of our proposed approach with existing look-alike modeling baselines.

    @inproceedings{doan2019adversarial,
      bibtex_show = {true},
      abbr = {CIKM},
      pdf = {https://dmkd.cs.vt.edu/papers/CIKM19.pdf},
      teaser = {doan2019adversarial.png},
      selected = {true},
      author = {Doan, Khoa D and Yadav, Pranjul and Reddy, Chandan K},
      title = {Adversarial Factorization Autoencoder for Look-Alike Modeling},
      year = {2019},
      isbn = {9781450369763},
      publisher = {Association for Computing Machinery},
      address = {New York, NY, USA},
      url = {https://doi.org/10.1145/3357384.3357807},
      doi = {10.1145/3357384.3357807},
      booktitle = {Proceedings of the 28th ACM International Conference on Information and Knowledge Management},
      pages = {2803–2812},
      numpages = {10},
      keywords = {deep learning, autoencoder, hashing, factorization, adversarial training, look-alike modeling},
      location = {Beijing, China},
      series = {CIKM '19}
    }