Noise Injection
Adding controlled, camera-like noise to an image to replace the sensor noise AI generators omit.
Noise injection is the deliberate addition of controlled noise to an image so it carries the kind of stochastic variation a real camera sensor produces. AI generators output images that are too clean in the wrong way — they lack the fine, structured sensor noise that every physical capture has, and that absence is one of the strongest detection signals.
Not all noise is equal. Adding random uniform noise is crude and detectable in its own right. Effective injection matches the spectral and spatial characteristics of real sensor noise: the right magnitude, the right correlation structure, and the right behavior across luminance levels. Done well, it sits below the human perception threshold while materially changing a detector's score.
Noise injection is related to but distinct from PRNU injection. PRNU is the specific, per-sensor fingerprint; general noise injection restores the broader sensor-noise floor. SynthGuard uses both — PRNU for the camera-identity signal and broader noise shaping for the overall floor.
In SynthGuard's pipeline, noise injection works alongside FFT disruption (which targets the frequency domain) and texture perturbation (which targets local statistics). Together they rebuild the noise characteristics of a genuine photograph without visible quality loss.
Tools that address Noise Injection
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