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Tanima Dutta

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3 papers

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AAAI Conference 2025 Conference Paper

Toward Improving Robustness and Accuracy in Unsupervised Domain Adaptation

Aishwarya Soni
Tanima Dutta

Adversarial robustness in the context of Unsupervised Domain Adaptation (UDA) is particularly challenging due to the lack of labels in the target domain. Pseudo labels are often used to make adversarial robust models but compromise robustness and accuracy, falling short of the performance due to noise and inaccuracies in these pseudo labels. The main challenges in achieving robustness and accuracy include ensuring reliable pseudo labels and developing effective training methods that bring alignment between clean and adversarial examples of target data. To address these challenges, we propose a novel training method within the self-training paradigm Consistent Attention Mapping with Self Pseudo Label Refinement (CAM+SPLR). It begins with the pre-training of the UDA model, resulting in a UDA pre-trained model, which is initialized into two separate models: the Anchor model and the TargetNet model. The Anchor model encourages the attention maps of clean images and their adversarial counterparts to be similar, while the TargetNet model simultaneously performs self-training using Adversarial target data and refining the pseudo labels. CAM+SPLR improves both semantically relevant key features and pseudo-labels through a two-step stochastic gradient descent process during training. We conducted extensive experiments on benchmark datasets, including OfficeHome, PACS, and VisDA, demonstrating significant improvements in both robustness and accuracy. Our method achieves an average accuracy improvement of 6% and 8.1% and an average robustness improvement of 10.2% and 4.9%, compared to state-of-the-art methods on the PACS and VisDA datasets.

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AAAI Conference 2024 Conference Paper

Stronger and Transferable Node Injection Attacks

Samyak Jain
Tanima Dutta

Despite the increasing popularity of graph neural networks (GNNs), the security risks associated with their deployment have not been well explored. Existing works follow the standard adversarial attacks to maximize cross-entropy loss within an L-infinity norm bound. We analyze the robustness of GNNs against node injection attacks (NIAs) in black-box settings by allowing new nodes to be injected and attacked. In this work, we propose to design stronger and transferable NIAs. First, we propose margin aware attack (MAA) that uses a maximum margin loss to generate NIAs. We then propose a novel margin and direction aware attack (MDA) that diversifies the initial directions of MAA attack by minimizing the cosine similarity of the injected nodes with respect to their respective random initialization in addition to the maximization of max-margin loss. This makes the NIAs stronger. We further observe that using L2 norm of gradients in the attack step leads to an enhanced diversity amongst the node features, thereby further enhancing the strength of the attack. We incorporate transferability in NIAs by perturbing the surrogate model before generating the attack. An analysis of eigen spectrum density of the hessian of the loss emphasizes that perturbing the weights of the surrogate model improves the transferability. Our experimental results demonstrate that the proposed resilient node injection attack (R-NIA) consistently outperform PGD by margins about 7-15% on both large and small graph datasets. R-NIA is significantly stronger and transferable than existing NIAs on graph robustness benchmarks.

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AAAI Conference 2023 Conference Paper

Leveraging Weighted Cross-Graph Attention for Visual and Semantic Enhanced Video Captioning Network

Deepali Verma
Arya Haldar
Tanima Dutta

Video captioning has become a broad and interesting research area. Attention-based encoder-decoder methods are extensively used for caption generation. However, these methods mostly utilize the visual attentive feature to highlight the video regions while overlooked the semantic features of the available captions. These semantic features contain significant information that helps to generate highly informative human description-like captions. Therefore, we propose a novel visual and semantic enhanced video captioning network, named as VSVCap, that efficiently utilizes multiple ground truth captions. We aim to generate captions that are visually and semantically enhanced by exploiting both video and text modalities. To achieve this, we propose a fine-grained cross-graph attention mechanism that captures detailed graph embedding correspondence between visual graphs and textual knowledge graphs. We have performed node-level matching and structure-level reasoning between the weighted regional graph and knowledge graph. The proposed network achieves promising results on three benchmark datasets, i.e., YouTube2Text, MSR-VTT, and VATEX. The experimental results show that our network accurately captures all key objects, relationships, and semantically enhanced events of a video to generate human annotation-like captions.

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