Inpainting Detection

Inpainting algorithms must synthesize new pixel values to fill a target region, leaving behind characteristic interpolation signatures. Unlike natural edges captured by a camera sensor, inpainted regions often exhibit over-smoothness or a lack of high-frequency texture. Forensic detectors analyze local variance and pixel correlation patterns to identify areas where texture complexity is statistically lower than the surrounding authentic regions, revealing the smooth, synthetic fill.

Exemplar-based inpainting methods copy pixel blocks from a known 'source' region to the unknown 'target' region. This creates detectable copy-move patterns within the same image. Forensic analysis involves searching for statistically identical blocks of pixels in different locations. The presence of large, perfectly replicated patches—especially with identical noise patterns—is a definitive marker of inpainting, as natural scenes never contain exact pixel-level duplication.

When an image is inpainted and re-saved, the manipulated region often possesses a different compression history than the original background. Detecting double JPEG compression or analyzing the quantization tables can reveal a mismatch. The inpainted area may lack the specific compression artifacts present in the authentic parts of the image, or it may exhibit a different primary quantization matrix, exposing the edit.

Every camera sensor introduces a unique, stable sensor pattern noise (SPN) across the entire image. Inpainting disrupts this uniform noise field. The reconstructed region will either be completely devoid of the camera's native noise fingerprint or will exhibit a synthetic, statistically uniform noise pattern. Subtracting the estimated noise residual from the image highlights the inpainted area as a flat, noiseless anomaly against the textured background.

Modern inpainting uses Generative Adversarial Networks (GANs) or diffusion models, which leave unique spectral fingerprints. These networks often introduce grid-like artifacts from transposed convolutions or specific frequency peaks invisible to the human eye. A frequency domain analysis using Discrete Fourier Transform (DFT) can reveal unnatural peaks in the magnitude spectrum that correspond to the neural network's upsampling kernel, identifying the region as synthetically generated.

While inpainting can remove objects convincingly, it often fails to reconstruct complex semantic relationships. Forensic analysis includes checking for physically impossible shadows, missing reflections, or broken object boundaries. For example, an inpainted region might remove a person but fail to reconstruct the occluded background pattern of a fence, leaving a nonsensical visual discontinuity that violates the scene's physical logic.

A foundational forensic method that resaves an image at a known quality level and computes the difference from the original. Regions with disproportionately high error levels indicate areas that have undergone different compression histories—a hallmark of splicing or inpainting. When an object is removed via inpainting, the reconstructed region often lacks the multi-generational compression artifacts present in the authentic background, creating a detectable ELA anomaly at the manipulation boundary.

Identifies duplicated pixel blocks within a single image by partitioning the image into overlapping blocks and searching for statistically similar regions. This technique directly counters a common inpainting precursor: the conceal-and-fill workflow where an attacker copies a clean patch from elsewhere in the image to cover an object before blending. Detection algorithms are robust to post-processing like rotation and scaling, using invariant feature transforms to match duplicated regions.

The forensic process of identifying boundaries where a region from a donor image has been inserted into a host image. Unlike inpainting, which synthesizes new pixels, splicing introduces foreign pixel statistics. Detection methods analyze:

• Noise variance inconsistencies across the image
• Color filter array (CFA) interpolation pattern disruptions
• JPEG ghost artifacts from mismatched quantization tables Inpainting is often used post-splicing to blend the inserted region, making combined detection critical.

The pixel-level task of generating a binary mask that precisely identifies manipulated regions, as opposed to providing a single global authenticity score. Modern deep learning approaches use encoder-decoder architectures to output high-resolution localization maps. Inpainting detection models are a specialized subset of tampering localization, trained specifically to recognize the interpolation textures and boundary discontinuities characteristic of inpainting algorithms rather than general splicing artifacts.

A deep learning-based camera model fingerprint that captures the local relationships between noise residuals and image semantics. Unlike PRNU, which identifies a specific sensor, Noiseprint extracts a model-level fingerprint that reveals anomalies when an image region deviates from the expected noise-to-texture relationship. Inpainted regions exhibit distinct noiseprint patterns because the synthetic content lacks the camera's native sensor noise characteristics, making this technique highly effective for localizing generative fill areas.

Identifies the statistical fingerprints left when a JPEG image is decompressed, manipulated, and saved again. The technique detects ghost peaks in DCT coefficient histograms caused by misalignment between primary and secondary quantization tables. Inpainting operations almost always require decompression of the source JPEG, pixel manipulation, and recompression—introducing double compression artifacts. The absence of these artifacts in a region surrounded by them is a strong indicator of localized inpainting.