Spotting and Correcting AI-Generated Facial Anomalies

machine-made face images have become increasingly popular for marketing campaigns, but they often come with disturbing facial distortions that can undermine credibility. These visual flaws are not always obvious at first glance, but upon closer inspection, they reveal the constraints of current deep learning systems.

major problems often seen are: eyes looking in different directions, asymmetrical features, waxy or glossy skin, extra or missing digits, and illogical shadow patterns that fail to align with the intended scene.

One of the most pervasive issues is the eye direction mismatch—where the one eye is offset, creating a strange, lifeless stare.

Another prevalent flaw is the blending of facial features, such as ear-jaw boundary blur, unexplained digits, or informative page brows placed too high or too low.

The dermis is often rendered as overly smooth or waxy, lacking the realistic fine details of micro-wrinkles and freckles that give a face depth and authenticity.

Hair often looks like a monolithic shape, or appear detached from the scalp, resulting in a plastic cap appearance.

Additionally, the jawline and cheekbones can be artificially accentuated or softened, leading to an artificial or gender-neutral appearance that fails to reflect uniqueness.

Shadows often defy physics; light casts from contradictory angles, or bright spots show up on shaded zones due to the direction of illumination.

To fix these distortions, users should generate several iterations from identical input to maximize the probability of authenticity.

Refining the prompt with specific descriptors such as "micro-detail skin", "natural facial variation", "diffused key light", and "authentic ocular shine" can improve output fidelity.

Post-generation editing using tools like Photoshop can adjust visual artifacts like awkwardly positioned brows, unequal shadow density, or artificial pigmentation.

Fine-tuning gaze direction, adding realistic moles or freckles, and redrawing strand flow can significantly enhance realism.

A useful technique is to the machine-made photo onto a authentic reference image with matching angle and illumination to spot inconsistencies.

When authenticity matters most, leveraging automation alongside human oversight remains the most reliable approach—using AI to accelerate the process while applying subjective refinement for truth.

In the end, perfection isn’t the target, but a believable human face that triggers emotional recognition, and that requires attention to detail, careful iteration, and an understanding of real human anatomy and expression.