Side-by-side comparison (Original vs Social Media Copy)
FFT Noise Pattern (frequency domain):
- Original: Noise is scattered more randomly, like what a camera sensor would produce. You see a gentle spread around the center with less obvious structure.
- Social Media Copy: Strong vertical and horizontal streaks appear, which look like algorithmic banding. These are typical of recompression and resizing that social platforms apply.
ELA (Error Level Analysis):
- Original: Compression inconsistencies are fairly even across the entire photo. Both subject and background show similar error levels, which is what you’d expect from a single capture.
- Social Media Copy: The subject (person) lights up disproportionately in ELA, while the background looks “muted.” This unevenness can trick detectors into thinking parts of the image were AI-generated or manipulated.
Why Hive flagged it
Since Hive connects directly to social media, the version it scanned is the uploaded one, not the original on your device.
- Social media platforms always reprocess photos: they resize, compress, sometimes even strip metadata.
- This reprocessing adds artifacts that don’t exist in your original.
- Those artifacts — banding, uneven recompression — can look a lot like the fingerprints of AI generators.
So it’s not that the photo was “distorted on its way out” when you downloaded it. The distortion happens on the way in, during upload. Hive then scans that version (the platform’s processed copy), which explains why it leaned toward “Gemini.”
