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Method Drift›Parameter-efficient fine-tuning (LoRA family)

Superseded baseline#234 of 1,113 most-superseded

frequency-based detectors

Parameter-efficient fine-tuning (LoRA family)

superseded — cited as a baseline and beaten by newer methods

2 papers critique it · 0 beat it on benchmarks

What papers say

Verbatim critique sentences, each from a paper that cites frequency-based detectors as a baseline.

  • “While these methods proved highly effective in controlled environments, they exhibited severe fragility when exposed to standard image processing operations. For instance, a simple JPEG compression pass acts as a low-pass filter, effectively erasing the high-frequency clues that frequency-based detectors depend upon”
    — Boosting Robust AIGI Detection with LoRA-based Pairwise Training
  • “Unlike GANs, diffusion models generate images via iterative denoising, resulting in fingerprints that resemble high-frequency Gaussian noise rather than structural periodicity. This shift renders frequency-based detectors less effective on modern deepfakes (e.g., Stable Diffusion rombach2022latentdiffusion, DALL-E openai2023dalle3)”
    — AdaptPrompt: Parameter-Efficient Adaptation of VLMs for Generalizable Deepfake Detection

Newer alternatives

Recent methods in the same sub-problem, not yet superseded in the knowledge base.