AIFIND: Artifact-Aware Interpreting Fine-Grained Alignment for Incremental Face Forgery Detection

2026-04-21 · Source: cs.AI updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cybersecurity & Data Privacy · Depth: Expert, long

Summary

AIFIND, or Artifact-Aware Interpreting Fine-Grained Alignment for Incremental Face Forgery Detection, is a novel data-replay-free framework designed to address feature drift and catastrophic forgetting in Incremental Face Forgery Detection (IFFD). Proposed by researchers from Harbin Institute of Technology, Weihai, AIFIND leverages semantic anchors to stabilize incremental learning by establishing a fixed coordinate system from low-level artifact cues. It comprises an Artifact-Driven Semantic Prior Generator (ASPG) for instantiating invariant semantic anchors, an Artifact-Probe Attention (APA) module to inject these anchors into the image encoder, and an Adaptive Decision Harmonizer (ADH) to preserve angular relationships of semantic anchors across tasks. Extensive experiments on multiple incremental protocols, including Protocol 1 (Datasets Incremental) and Protocol 2 (Forgery Categories Incremental) using datasets like SDv21, FF++, DFDCP, CDF, MCNet, BlendFace, and StyleGAN3, validate AIFIND's superiority, achieving the best performance in AUC.

Key takeaway

For Computer Vision Engineers developing robust deepfake detection systems, AIFIND offers a compelling data-replay-free paradigm. You should consider integrating artifact-aware semantic anchoring to mitigate catastrophic forgetting and feature drift in incremental learning scenarios, potentially reducing storage requirements and improving adaptability to emerging forgery types. Evaluate its performance against existing replay-based methods for your specific deployment needs.

Key insights

AIFIND uses artifact-driven semantic anchors and attention mechanisms to stabilize incremental face forgery detection without data replay.

Principles

• Linguistic concepts offer invariance for region-aware authenticity priors.
• Explicitly constraining feature space with stable anchors prevents drift.
• Spherical semantic alignment maintains geometric consistency across tasks.

Method

AIFIND generates semantic anchors from artifact cues, injects them into a vision transformer via Artifact-Probe Attention, and harmonizes classifiers by preserving angular relationships of these anchors.

In practice

• Utilize MediaPipe Face Mesh for facial region localization.
• Employ LLMs to generate contrastive text pairs for semantic anchor selection.
• Implement dual supervision for global authenticity and specific artifact prediction.

Topics

• AIFIND
• Incremental Face Forgery Detection
• Semantic Anchors
• Artifact-Probe Attention
• Adaptive Decision Harmonizer

Best for: Computer Vision Engineer, Research Scientist, AI Scientist, Machine Learning Engineer, AI Security Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.AI updates on arXiv.org.