An analysis of automatic image filtering on WeChat Moments

WeChat's OCR filter performs blob merging to reconstruct characters from disconnected components. Filling character strokes with tiled letter patterns evaded OCR filtering in 100% of tests (vs. 92% for tiled square patterns), because tiled letters distract the blob-merging stage into finding the letter tiles rather than the composed characters.

§3.1.2 detection keyword-filtering cn

WeChat's OCR-based image filter converts images to grayscale using the luminosity formula (0.299r + 0.587g + 0.114b) before text recognition. All 150 test images with colored text on a luminosity-matched gray background evaded OCR filtering, while average and lightness formulas failed to evade filtering for most colors.

§3.1.1 detection keyword-filtering cn

WeChat normalizes uploaded images by their shortest dimension before blacklist comparison. Adding blank space equal to 50–200% of the longest dimension caused 4/5 wide images and 3/5 tall images to evade filtering; adding space along the shortest dimension never evaded filtering, consistent with a shortest-dimension resize hypothesis.

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WeChat's visual-based image filter compares uploads against a specific blacklist using a perceptual similarity metric rather than ML classification. Semantic-preserving transformations — mirroring, cropping, adding whitespace — evaded all 15 tested blacklisted images, and images filtered visually were typically removed within 10 seconds, faster than OCR-filtered images (5–30 seconds).

§3.2.2 detection keyword-filtering cn

WeChat's visual filter compares images as a whole rather than via a sliding window. Adding 3 or more duplicate copies of a blacklisted image to its canvas caused 8/10 images to evade filtering with no evasion attributable to compression artifacts (Figure A.13), whereas blank canvas extensions evaded mostly only through compression artifacts.

§3.2.5 detection keyword-filtering cn