Image Error Level Analysis (ELA) & Deepfake Forensics Inspector (2026)

JPEG compression operates on independent 8x8 pixel blocks. Each time an image is saved, every block approaches a local error minimum at that quality rate. If a region is spliced in from another photo or generated by an AI inpainting model, its 8x8 blocks sit at a different compression cycle and light up brighter when re-compressed at 90%–95% quality and multiplied against the original.

Camera sensors introduce uniform Photo-Response Non-Uniformity (PRNU) and Bayer demosaicing noise across the entire frame. Generative AI models (Stable Diffusion, Flux, GAN face-swappers) synthesize pixels with unnatural high-frequency spectral roll-off or localized mask boundaries that stand out clearly when inspected with ELA and high-pass residual filters.

Yes, if the PNG or WebP previously underwent lossy JPEG compression before being saved as lossless PNG, the underlying 8x8 DCT block artifacts remain baked into the pixel grid and will still surface during Error Level Analysis.

High-frequency transitions (such as sharp text, eyelashes, or dark borders against white backgrounds) naturally experience higher quantization error during JPEG compression. Always compare similar textures (e.g., skin vs. skin, or sky vs. sky) rather than mistaking sharp high-contrast edges for tampering.

No. Every stage—binary header parsing, HTML5 Canvas JPEG re-encoding, pixel residual math, and heatmap rendering—executes 100% locally inside your browser tab.