Contents
It depends on the edit far more than on how big the edit is. The research record splits everyday audio edits cleanly into two groups: edits that move or mask frequency content, which can push a detector from near-perfect toward coin-flip, and edits that only touch timing or ambience, which barely register. A two-semitone pitch shift does more damage than doubling a song’s length. Knowing which group an edit belongs to tells you how much a detector score on an edited track is still worth. This article is broader than does MP3 compression defeat AI music detectors?, which is the compression case in detail; compression is one axis here among several, and pitch is the sharper one.
The edits that break detectors
Pitch shifting is the most efficient wrecker on record. Afchar, Meseguer-Brocal and Hennequin (2024) measured a detector scoring 99.8 percent on clean audio falling to 66.6 percent under a two-semitone shift, and Sroka’s robustness study found a downward two-semitone shift alone flipped the SONICS-family detector’s verdict on AI tracks to “highly real” (Sroka et al., 2025, ISMIR Late-Breaking Demo). Low-bitrate re-encoding sits close behind. MusicDET, one of 2026’s strongest detectors at a 4.51 percent equal error rate on clean audio, degrades to 41.75 percent under MP3 at 64 kbps, 35.85 percent under AAC, and 22.15 percent under Opus, with white noise pushing it to 44.11 percent and the same pitch shift to 44.73 percent (Han, Wang, Gui, 2026, arXiv:2605.18072). Even a plain resample qualifies: a commercial detector fooled by resampling to 22.05 kHz then misclassified two of five human Million Song Dataset tracks, three of five Udio tracks, and five of five Suno tracks (Cros Vila et al., 2025, TISMIR). None of these edits is exotic. They are what happens to a track in normal distribution.
The edits that don’t
The same measurements show a second group of edits doing almost nothing. Afchar’s detector, the one that lost a third of its accuracy to a pitch shift, “remains robust to time stretch” in the authors’ words, holding 88.6 percent under time-stretching and 96.9 percent through added reverb, with EQ similarly harmless. The 2026 mixture-of-experts detector Sofia loses only about 1.3 points of F1 under time-stretch, its most stable condition. So the intuition “any edit degrades the detector” is wrong in both directions: a producer’s timing tweaks, ambience, and tonal shaping leave detection essentially intact, while a single frequency-domain operation can hollow it out.
Why the split falls where it does
The pattern follows where the evidence lives. The dominant detector families read fine frequency structure: the spectral fingerprint a generator’s codec stamps at fixed frequencies, the spectral statistics a density model learned from real music. Pitch shifting slides that structure off the bins a detector learned; low-bitrate codecs discard the high-frequency band much of it sits in; resampling redraws the frequency grid; noise buries the residual. Time-stretching, EQ, and reverb reshape the audio around that structure without destroying it. Li, Chen and Wei (2025) found detectors most vulnerable to compression when neural codecs are involved, which re-stamp a different codec’s signature over the original. The point is that compression is one axis among several, and the frequency-domain edits are the ones to watch.
Detectors are learning to cope, unevenly
The damage is not uniform across detector families, and the differences are instructive. ArtifactNet, trained on WAV, MP3, AAC and Opus versions of every training example, cut its cross-codec performance drift by 83 percent, from 0.95 to 0.16, and holds a 99.12 percent true-positive rate at MP3 128 kbps (Oh, 2026): training on the damage buys real resistance to that damage, though only for the damage trained on. Stem processing is a partial dent rather than a full erase; ArtifactNet falls from 0.9950 to 0.9592 F1 after a single Demucs separation pass. Lyrics-based detectors sidestep the audio problem entirely, since transcribed words survive processing that destroys spectral evidence; Frohmann and colleagues report their lyrics classifier holding around 85 to 90 percent under conditions that collapse signal readers (ISMIR 2025). A track’s verdict is therefore most trustworthy where families that read different evidence agree, and least where a single fragile reader is doing all the work, the theme of why music detectors disagree.
Mastering and broadcast are their own cases
Two everyday conditions sit at the edges of what the research can quantify. Mastering is one: human mastering applied to an AI track is a documented evasion surface (Go and Kim, 2026, HAIM), and there is no dedicated mastering-statistics detector yet, so a professionally finished AI master is genuinely harder than a raw generator export. Broadcast is the other, and it is less an edit than a context: short excerpts and music under speech starve the long-context detectors of the song structure they read, and the best detector on a broadcast benchmark managed only 61.1 percent F1, falling to 33.2 percent on low-level background music (López-Ayala et al., 2026). A score on a TikTok clip, a radio bed, or a stream rip should be read differently from a score on the original full-length file.
What this means for reading a score
First, an edited track weakens the verdict in both directions: editing can hide an AI track’s evidence, and it can also distort a real track toward a false flag, the problem covered in do AI music detectors falsely flag real music?. Second, whenever possible, test the original export, not a copy that has been through a platform, a video rip, or a pitch-shifted repost. Third, ask what has happened to the file since creation: a verdict on an unedited original from a known generator generation is operating near its benchmark conditions; a verdict on a shifted, re-encoded, resampled, or mastered copy is operating in the conditions where the published numbers fall to the sixties and below. The deliberate version of this fragility, and how little it takes to exploit it, is the subject of can AI music detectors be fooled?.
Sources
- Afchar, Meseguer-Brocal, Hennequin (2024). Detecting Music Deepfakes Is Easy but Actually Hard. arXiv:2405.04181.
- Sroka, Wężowicz, Sidorczuk, Modrzejewski (2025). Evaluating Fake Music Detection under Audio Augmentations. ISMIR 2025 Late-Breaking Demo. arXiv:2507.10447.
- Han, Wang, Gui (2026). MusicDET: Zero-Shot AI-Music Detection via Frequency-Guided Normalizing Flows. arXiv:2605.18072.
- Cros Vila, Sturm, Casini, Dalmazzo (2025). The AI Music Arms Race: On the Detection of AI-Generated Music. TISMIR 8(1). DOI:10.5334/tismir.254.
- Oh (2026). ArtifactNet: Forensic Codec-Residual Detection of AI-Generated Music. arXiv:2604.16254.
- Li, Chen, Wei (2025). On the vulnerability of AI-music detectors to compression. arXiv:2503.17577.
- Go, Kim (2026). HAIM: Detecting Human-AI Mixed Music. arXiv:2606.01686.
- Frohmann, Epure, Meseguer-Brocal, Schedl, Hennequin (2025). AI-Generated Song Detection via Lyrics Transcripts. ISMIR 2025.
- López-Ayala, Cabello, Zinemanas, Molina, Rocamora (2026). AI-Generated Music Detection for Broadcast Monitoring. ICASSP 2026. arXiv:2602.06823.