Research


Research interests

Security and forensic reliability of machine-learning systems, especially provenance and attribution of AI-generated content: LLM and image watermarking, the robustness of detection under removal and forgery attacks, and the gap between statistical detection performance and evidentiary standards. Adjacent interests in digital forensics methodology, applied systems security, and how security claims about ML systems are evaluated and reproduced.

Publications


For research applications, see my academic CV.

First-author preprint · cs.CR · Sep 2026

Forging Tree-Ring: Reproducing and Instrumenting Black-Box Semantic Watermark Forgery

S. R. Tamim, M. T. H. Toufique, A. M. T. Islam

Tree-Ring hides a watermark in the starting noise of an image diffusion model. Earlier work showed that an attacker without the key can forge it, producing images the real detector accepts. We reproduced that forgery attack against Tree-Ring on Stable Diffusion XL with the original authors’ code, on free-tier GPUs with far less memory than the original study, and instrumented the detector along the way.

Method

  • Reproduced the Reprompt forgery attack of Müller et al. against Tree-Ring on Stable Diffusion XL using the authors’ released code.
  • Ran it on free-tier dual NVIDIA T4 GPUs (14.6 GB usable per device) instead of the A40 hardware used originally, patching the pipeline’s direct autoencoder calls to run SDXL in half precision and confirming with a controlled probe that the detector statistic is unchanged.
  • Recovered the non-central χ² statistic that the released detector discards (it returns only the CDF); the recovery matches the released detector exactly.

Key results

  • The attack reproduces: across six trials of three arms, genuine images were detected 6/6, clean images 0/6 and forged images 5/6, at 325–332 s per attack.
  • Two scores built from the recovered statistic separate forged from clean images at AUC 0.861 and 0.972 on the same 18 observations.
  • A prediction made from reading the detector source was contradicted by the measurements, and the paper reports it.

Limitations

This reproduces an existing attack rather than proposing a new one. It covers one model (Stable Diffusion XL) and one watermark (Tree-Ring), with six trials per arm, so the AUC figures rest on 18 observations.

Reproducibility

The notebook, the pinned fork and every measurement artifact are released with the paper so each number can be checked.

BibTeX
@misc{tamim2026forging,
  title         = {Forging Tree-Ring: Reproducing and Instrumenting Black-Box Semantic Watermark Forgery},
  author        = {Tamim, Saifur Rahman and Toufique, Md Taslimul Hasan and Islam, A. M. Tayeful},
  year          = {2026},
  eprint        = {2609.12909},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CR},
  doi           = {10.48550/arXiv.2609.12909},
  url           = {https://arxiv.org/abs/2609.12909}
}

First-author preprint · cs.CR · Jul 2026

AI Watermark Evidence Fails Forensic Readiness: An Empirical Evaluation

S. R. Tamim, A. L. Khan

Watermarking laws such as the EU AI Act and California SB 942 assume that detecting a watermark produces evidence a court can rely on. This work tests that assumption. I proposed a Forensic Readiness Score that maps watermark detection onto the Daubert admissibility criteria and the NIST SP 800-86 forensic process, then attacked three schemes with meaning-preserving paraphrase.

Method

  • Evaluated KGW, Unigram and SynthID-Text (MarkLLM implementation) across 846 valid paraphrase runs over 15 prompts per method.
  • KGW and Unigram on Qwen2.5-1.5B (NVIDIA T4); SynthID-Text on Gemma-2-9b-it (NVIDIA A100).
  • Counted a removal only when sentence embeddings (all-MiniLM-L6-v2) confirmed the paraphrase preserved meaning.

Key results

  • Paraphrasing removed the watermark from every initially detected KGW and Unigram text (100%) and from 98.3% of SynthID texts.
  • Pre-attack false-negative rates were already high: KGW 70%, Unigram 83%, SynthID 80%.
  • SynthID flagged 5.4% of paraphrased human-written controls as AI-generated.
  • No method satisfied more than two of five Daubert factors.

Limitations

The point-based part of the score, though working as designed, cannot fully express forensic uselessness. The paper reports this as guidance for future framework design rather than leaving it out.

Read on arXivDOI
BibTeX
@misc{tamim2026watermark,
  title         = {AI Watermark Evidence Fails Forensic Readiness: An Empirical Evaluation},
  author        = {Tamim, Saifur Rahman and Khan, Amir Labib},
  year          = {2026},
  eprint        = {2607.16010},
  archivePrefix = {arXiv},
  primaryClass  = {cs.CR},
  doi           = {10.48550/arXiv.2607.16010},
  url           = {https://arxiv.org/abs/2607.16010}
}

Education


BSc in Computer Science & Engineering

Northern University of Bangladesh, Dhaka

Thesis: AI Watermark Evidence Fails Forensic Readiness: An Empirical Evaluation. Supervised by Amir Labib Khan.

CGPA 3.90 / 4.00Expected 2026 · all coursework complete
  • Higher Secondary Certificate (Science)

    Bangladesh Navy School & College, Dhaka

    2021GPA 5.00 / 5.00
  • Secondary School Certificate (Science)

    Bangladesh Navy School & College, Dhaka

    2019GPA 5.00 / 5.00

Activities & honours


Honours

1
  • Dean’s Merit List

    Northern University of Bangladesh

    Awarded a tuition waiver and a merit scholarship.

    Ranked 1st, 3rd semester

Contests

1 of 3
  • NUBCC Ultimate Programming Contest 2025

    NUB Computer Club

    C++.

    Participant
  • Delta Dev Codestorm, software development contest

    NUB CSE Funathon Fiesta 2025, Northern University of Bangladesh

    Built with Node.js and the Google Gemini API.

    1st Runner-Up
  • NUBCC Programming Contest 2024

    NUB Computer Club

    C++.

    Participant

Hackathons

3
  • Greencon Hackathon 2025

    Greentech

    Built with NestJS and PostgreSQL.

    Participant
  • Neofetch Hackathon 2025 (INVENTIOUS 4.1)

    MIST Innovation Club, Military Institute of Science and Technology, with Programming Hero

    Built with React and PostgreSQL.

    Participant
  • Agentic AI Innovation Challenge 2025

    Ready Tensor, Inc.

    Built with the Google Gemini API.

    Participant

CTF

2
  • UAP Cyber Siege 2025

    Cyber Security Club, Dept. of CSE, University of Asia Pacific

    On-site capture-the-flag contest.

    Participant
  • picoCTF practice

    Carnegie Mellon University

    Ongoing self-directed practice in web exploitation and cryptography.

Certifications

0 of 5
  • Supervised Learning with scikit-learn

    DataCamp

    4-hour course · Statement of Accomplishment #48,879,432 · completed 19 Jul 2026.

  • Intermediate Python

    DataCamp

    4-hour course · Statement of Accomplishment #48,565,581 · completed 2 Jul 2026.

  • Introduction to Python

    DataCamp

    4-hour course · Statement of Accomplishment #48,255,274 · completed 22 Jun 2026.

  • API Security in the World of DevSecOps

    APIsec University

    2-hour course.

    Completed
  • Cyber Hygiene Training

    The Asia Foundation & SAJIDA Foundation, supported by Google.org

    Completed

Events

0 of 2
  • Bear Summit & National Semiconductor Symposium 2025

    ICT Division, Bangladesh Computer Council and EDGE

    Registered
  • AWSome Day Online Conference

    Amazon Web Services

    Attended

Interested in this work?

Read the academic CV, or get in touch to talk about research collaboration.