Md Awsafur Rahman

2026

1. 

   LikeBench: Evaluating Subjective Likability in LLMs for Personalization

   Md Awsafur Rahman, Adam Gabryś, Daniel Kang, and 3 more authors

   Preprint, 2026

   Abs arXiv Bib

   LikeBench is a novel benchmark for evaluating subjective likability in large language models for personalization applications.

   @article{rahman2025likebench,
     title = {LikeBench: Evaluating Subjective Likability in LLMs for Personalization},
     author = {Rahman, Md Awsafur and Gabryś, Adam and Kang, Daniel and Tan, Tianjian and Sun, Jiayi and Chandramouli, Ashwin},
     journal = {Preprint},
     year = {2026},
   }

2. 

   Hyperspectral Trajectory Image for Multi-Month Trajectory Anomaly Detection

   Md Awsafur Rahman, Chandrakanth Gudavalli, Hardik Prajapati, and 1 more author

   Preprint, 2026

   Abs arXiv Bib

   Trajectory anomaly detection underpins applications from fraud detection to urban mobility analysis. Dense GPS methods preserve fine-grained evidence such as abnormal speeds and short-duration events, but their quadratic cost makes multi-month analysis intractable; consequently, no existing approach detects anomalies over multi-month dense GPS trajectories. The field instead relies on scalable sparse stay-point methods that discard this evidence, forcing separate architectures for each regime and preventing knowledge transfer. We argue this bottleneck is unnecessary: human trajectories, dense or sparse, share a natural two-dimensional cyclic structure along within-day and across-day axes. We therefore propose TITAnD (Trajectory Image Transformer for Anomaly Detection), which reformulates trajectory anomaly detection as a vision problem by representing trajectories as a Hyperspectral Trajectory Image (HTI): a day x time-of-day grid whose channels encode spatial, semantic, temporal, and kinematic information from either modality, unifying both under a single representation. Under this formulation, agent-level detection reduces to image classification and temporal localization to semantic segmentation. To model this representation, we introduce the Cyclic Factorized Transformer (CFT), which factorizes attention along the two temporal axes, encoding the cyclic inductive bias of human routines, while reducing attention cost by orders of magnitude and enabling dense multi-month anomaly detection for the first time. Empirically, TITAnD achieves the best AUC-PR across sparse and dense benchmarks, surpassing vision models like UNet while being 11-75x faster than the Transformer with comparable memory, demonstrating that vision reformulation and structure-aware modeling are jointly essential. Code will be made public soon.

   @article{rahman2026titand,
     title = {Hyperspectral Trajectory Image for Multi-Month Trajectory Anomaly Detection},
     author = {Rahman, Md Awsafur and Gudavalli, Chandrakanth and Prajapati, Hardik and Manjunath, B. S.},
     journal = {Preprint},
     year = {2026},
   }

2025

1. 

   SONICS: Synthetic Or Not - Identifying Counterfeit Songs

   Md Awsafur Rahman, Zaber Ibn Abdul Hakim, Najibul Haque Sarker, and 2 more authors

   International Conference on Learning Representations (ICLR), 2025

   Abs arXiv Bib HTML Code Dataset

   The recent surge in AI-generated songs presents exciting possibilities and challenges. These innovations necessitate the ability to distinguish between human-composed and synthetic songs to safeguard artistic integrity and protect human musical artistry. Existing research and datasets in fake song detection only focus on singing voice deepfake detection (SVDD), where the vocals are AI-generated but the instrumental music is sourced from real songs. However, these approaches are inadequate for detecting contemporary end-to-end artificial songs where all components (vocals, music, lyrics, and style) could be AI-generated. Additionally, existing datasets lack music-lyrics diversity, long-duration songs, and open-access fake songs. To address these gaps, we introduce SONICS, a novel dataset for end-to-end Synthetic Song Detection (SSD), comprising over 97k songs (4,751 hours) with over 49k synthetic songs from popular platforms like Suno and Udio. Furthermore, we highlight the importance of modeling long-range temporal dependencies in songs for effective authenticity detection, an aspect entirely overlooked in existing methods. To utilize long-range patterns, we introduce SpecTTTra, a novel architecture that significantly improves time and memory efficiency over conventional CNN and Transformer-based models. For long songs, our top-performing variant outperforms ViT by 8% in F1 score, is 38% faster, and uses 26% less memory, while also surpassing ConvNeXt with a 1% F1 score gain, 20% speed boost, and 67% memory reduction.

   @article{rahman2024sonics,
     title = {SONICS: Synthetic Or Not - Identifying Counterfeit Songs},
     author = {Rahman, Md Awsafur and Hakim, Zaber Ibn Abdul and Sarker, Najibul Haque and Paul, Bishmoy and Fattah, Shaikh Anowarul},
     journal = {International Conference on Learning Representations (ICLR)},
     doi = {10.48550/arXiv.2408.14080},
     year = {2025},
     dataset = {https://huggingface.co/datasets/awsaf49/sonics},
   }

2024

1. 

   Semi-Supervised Semantic Depth Estimation using Symbiotic Transformer and NearFarMix Augmentation

   Md Awsafur Rahman, and Shaikh Anowarul Fattah

   WACV, 2024

   Abs arXiv Bib HTML YouTube Poster

   In computer vision, depth estimation is crucial for domains like robotics, autonomous vehicles, augmented reality, and virtual reality. Integrating semantics with depth enhances scene understanding through reciprocal information sharing. However, the scarcity of semantic information in datasets poses challenges. Existing convolutional approaches with limited local receptive fields hinder the full utilization of the symbiotic potential between depth and semantics. This paper introduces a dataset-invariant semi-supervised strategy to address the scarcity of semantic information. It proposes the Depth Semantics Symbiosis module, leveraging the Symbiotic Transformer for achieving comprehensive mutual awareness by information exchange within both local and global contexts. Additionally, a novel augmentation, NearFarMix is introduced to combat overfitting and compensate both depth-semantic tasks by strategically merging regions from two images, generating diverse and structurally consistent samples with enhanced control. Extensive experiments on NYU-Depth-V2 and KITTI datasets demonstrate the superiority of our proposed techniques in indoor and outdoor environments.

   @article{rahman2023sytra,
     title = {Semi-Supervised Semantic Depth Estimation using Symbiotic Transformer and NearFarMix Augmentation},
     author = {Rahman, Md Awsafur and Fattah, Shaikh Anowarul},
     journal = {WACV},
     year = {2024},
     organization = {IEEE/CVF},
     doi = {10.48550/arXiv.2308.14400},
     youtube = {-5YidVH9wIA},
   }

2023

1. 

   DwinFormer: Dual Window Transformers for End-to-End Monocular Depth Estimation

   Md Awsafur Rahman, and Shaikh Anowarul Fattah

   IEEE Sensors Journal, 2023

   Abs arXiv Bib HTML

   Depth estimation from a single image is of paramount importance in the realm of computer vision, with a multitude of applications. Conventional methods suffer from the trade-off between consistency and fine-grained details due to the local-receptive field limiting their practicality. This lack of long-range dependency inherently comes from the convolutional neural network part of the architecture. In this paper, a dual window transformer-based network, namely DwinFormer, is proposed, which utilizes both local and global features for end-to-end monocular depth estimation. The DwinFormer consists of dual window self-attention and cross-attention transformers, Dwin-SAT and Dwin-CAT, respectively. The Dwin-SAT seamlessly extracts intricate, locally aware features while concurrently capturing global context. It harnesses the power of local and global window attention to adeptly capture both short-range and long-range dependencies, obviating the need for complex and computationally expensive operations, such as attention masking or window shifting. Moreover, Dwin-SAT introduces inductive biases which provide desirable properties, such as translational equvariance and less dependence on large-scale data. Furthermore, conventional decoding methods often rely on skip connections which may result in semantic discrepancies and a lack of global context when fusing encoder and decoder features. In contrast, the Dwin-CAT employs both local and global window cross-attention to seamlessly fuse encoder and decoder features with both fine-grained local and contextually aware global information, effectively amending semantic gap. Empirical evidence obtained through extensive experimentation on the NYU-Depth-V2 and KITTI datasets demonstrates the superiority of the proposed method, consistently outperforming existing approaches across both indoor and outdoor environments.

   @article{rahman2023dwinformer,
     title = {DwinFormer: Dual Window Transformers for End-to-End Monocular Depth Estimation},
     author = {Rahman, Md Awsafur and Fattah, Shaikh Anowarul},
     journal = {IEEE Sensors Journal},
     year = {2023},
     doi = {10.1109/JSEN.2023.3299782},
     url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=zrxJCYIAAAAJ&citation_for_view=zrxJCYIAAAAJ:IjCSPb-OGe4C},
   }

2. 

   ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection

   Md Awsafur Rahman, Bishmoy Paul, Najibul Haque Sarker, and 2 more authors

   ICIP, 2023

   Abs arXiv Bib HTML YouTube Code Dataset Poster

   Synthetic image generation has opened up new opportunities but has also created threats in regard to privacy, authenticity, and security. Detecting fake images is of paramount importance to prevent illegal activities, and previous research has shown that generative models leave unique patterns in their synthetic images that can be exploited to detect them. However, the fundamental problem of generalization remains, as even state-of-the-art detectors encounter difficulty when facing generators never seen during training. To assess the generalizability and robustness of synthetic image detectors in the face of real-world impairments, this paper presents a large-scale dataset named ArtiFact, comprising diverse generators, object categories, and real-world challenges. Moreover, the proposed multi-class classification scheme, combined with a filter stride reduction strategy addresses social platform impairments and effectively detects synthetic images from both seen and unseen generators. The proposed solution outperforms other teams by 8.34% on Test 1, 1.26% on Test 2, and 15.08% on Test 3 in the IEEE VIP CUP at ICIP 2022.

   @article{rahman2023artifact,
     title = {ArtiFact: A Large-Scale Dataset with Artificial and Factual Images for Generalizable and Robust Synthetic Image Detection},
     author = {Rahman, Md Awsafur and Paul, Bishmoy and Sarker, Najibul Haque and Hakim, Zaber Ibn Abdul and Fattah, Shaikh Anowarul},
     journal = {ICIP},
     year = {2023},
     doi = {10.1109/ICIP49359.2023.10222083},
     url = {https://scholar.google.com/citations?view_op=view_citation&hl=en&user=zrxJCYIAAAAJ&citation_for_view=zrxJCYIAAAAJ:qjMakFHDy7sC},
     dataset = {https://www.kaggle.com/datasets/awsaf49/artifact-dataset},
     youtube = {hGXufeubNME},
   }