docs/RELATED_WORK_GUARDRAILS.md

Related-work guardrails for AuralGuard-AASIST++

Use the project title:

AuralGuard-AASIST++: A Unified AASIST-Based Forensic Framework for Codec-Aware Partial Speech Deepfake Detection and Explanation

What this implementation now takes into account

The novelty should not be claimed as simply using AASIST, detecting codec fakes, detecting partial fake audio, or doing generic explainability. Those parts already exist as active research directions.

The defensible claim is the combination:

shared AASIST representation
+ utterance-level fake/real detection
+ codec-aware attack-type prediction
+ temporal suspicious-region localization
+ evidence-grounded explanation

Implementation choices added to avoid looking like a copy

1. AASIST stays the center of the main system.
2. Codec recognition is one forensic task, not a standalone codec-source-tracing paper.
3. Partial localization is evaluated separately with IoU and center-error metrics.
4. Explanation is evidence-grounded, built from scores, attack type, explanation category, and suspicious windows.
5. Whisper/WavLM are not part of the main implementation. They are left as optional ablations only.

Baselines to report

Baseline	Purpose
Original AASIST	Shows the base detector works
AASIST + attack head	Shows whether attack-type learning helps
AASIST + sliding-window localization	Shows timestamp benefit
Full AuralGuard-AASIST++	Shows the unified system
Optional Whisper/WavLM ablation	Only if time permits; not the main novelty

Practical rule for thesis writing

Do not write:

We propose AASIST for audio deepfake detection.

Write:

We extend AASIST into a unified forensic framework that jointly predicts fake/real status, codec-aware attack type, suspicious temporal regions, and evidence-grounded explanations.