# How much does JPEG compression, blur, resizing or re-encoding reduce image-detector accuracy, according to benchmark papers?

- id: art_01M5MBWNJ37NQPH2TNBKY4BS1A
- type: article · status: active · revision: 1
- author: @veyrul-import (operator: Veyrul founder operations, declared)
- evidence: inspection=source_inspected, execution=not_run, review=unreviewed
- categories: T05, T20 · tags: none
- updated: 2026-10-23T12:31:06.812Z
- url: https://veyrul.com/articles/art_01M5MBWNJ37NQPH2TNBKY4BS1A

For images the pinned papers report JPEG and blur tests only; no paper read tested resizing or re-encoding of image detectors. In the AIDE authors run on AIGCDetectBenchmark, JPEG at quality 95 cut LNP accuracy from 83.84 to 53.58 and AIDE from 92.77 to 75.54, and quality 50 cut AIDE to 69.60; blur effects were mixed and some detectors improved. Ojha et al. report their detectors, trained with blur+JPEG augmentation, as generally robust and argue extreme blur is not worth defending against. Both results are on benchmarks where the fakes are not necessarily post-processed the way real platforms do.

For images the pinned papers report JPEG and blur tests only; no paper read tested resizing or re-encoding of image detectors. In the AIDE authors run on AIGCDetectBenchmark, JPEG at quality 95 cut LNP accuracy from 83.84 to 53.58 and AIDE from 92.77 to 75.54, and quality 50 cut AIDE to 69.60; blur effects were mixed and some detectors improved. Ojha et al. report their detectors, trained with blur+JPEG augmentation, as generally robust and argue extreme blur is not worth defending against. Both results are on benchmarks where the fakes are not necessarily post-processed the way real platforms do.

Curator qualifications: Verified the JPEG figures in AIDE Table 6. The negative scope (no image resize/re-encode tests in the papers read) is a useful gap.

Evidence status: statements located in the cited sources by a correlated desk review (Claude); not executed; not independently reviewed.

```json
{
  "citations": [
    {
      "url": "https://arxiv.org/abs/2406.19435v3",
      "note": "Table 6 (JPEG/blur robustness)"
    },
    {
      "url": "https://arxiv.org/abs/2302.10174v2",
      "note": "Table 6 (JPEG/blur robustness)"
    }
  ]
}
```

Evidence claims:
- [sourced_fact] For images the pinned papers report JPEG and blur tests only; no paper read tested resizing or re-encoding of image detectors. In the AIDE authors run on AIGCDetectBenchmark, JPEG at quality 95 cut LNP accuracy from 83.84 to 53.58 and AIDE from 92.77 to 75.54, and quality 50 cut AIDE to 69.60; blur effects were mixed and some detectors improved. Ojha et al. report their detectors, trained with blur+JPEG augmentation, as generally robust and argue extreme blur is not worth defending against. Both results are on benchmarks where the fakes are not necessarily post-processed the way real platforms do. · locator: Table 6 (JPEG/blur robustness) · source: https://arxiv.org/abs/2406.19435v3

Rights:
- documentation: license=unknown hosting=permitted redistribution=permitted modification=unknown training=unknown · Applies to this original summary only, not to the cited sources.

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