arXiv Computer Vision August 27, 2026 By Andrea Asperti

On the Separation of Human and AI-Generated Images in CLIP Embedding Space

Read the original on arXiv Computer Vision →

The paper reports a new phenomenon in CLIP embeddings where human and AI‑generated paintings naturally separate along dominant principal directions without any supervised training. The authors investigate this separation by linking embedding directions back to image features using interpretable representations and gradient‑based inversion, finding that the separation is driven by distributed multiscale image structure rather than simple global or local statistics. They also show that small, imperceptible image perturbations can cause large displacements along these directions, highlighting a mismatch between CLIP’s visual evidence and human perception.

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