CFA Interpolation Detection

The core forensic task involves blindly estimating the interpolation coefficients used by the camera's onboard processor. Each camera model uses a proprietary demosaicing algorithm that leaves a unique statistical signature. By solving a linear regression problem on local pixel neighborhoods, forensic tools can reconstruct the expected correlation weights. Deviations from this expected kernel in specific image regions suggest that the original CFA pattern has been disrupted by splicing or copy-move operations.

A forensic examiner generates a variance map of the prediction error after re-interpolating the image with the estimated kernel. In authentic regions, the error is uniformly low because the pixel values follow the camera's native interpolation model. In tampered regions, the error spikes dramatically because the foreign pixels do not obey the expected local correlation. This produces a heatmap of statistical anomalies that directly localizes the forgery without requiring any reference image.

Demosaicing algorithms introduce periodic artifacts at specific frequencies corresponding to the CFA layout. By transforming the image into the frequency domain via a Fourier transform, forensic tools can detect the presence or absence of these characteristic peaks. Tampering operations such as resizing, rotation, or splicing destroy these subtle periodic structures. The absence of expected CFA peaks in a region that should contain them is a strong indicator of manipulation.

In a standard Bayer CFA, the green channel is sampled at twice the density of red and blue. This creates a unique inter-pixel correlation structure where green pixels act as a high-frequency luminance proxy. Forensic algorithms exploit this by comparing the statistical distribution of green pixel differences against the expected model. A mismatch indicates that the green channel's native sampling pattern has been altered, often revealing sophisticated splicing attempts that tried to blend color channels.

The interpolation process forces a linear dependence between neighboring pixels, making the local covariance matrix of pixel values rank-deficient. By computing the covariance structure of small image blocks and measuring its singularity, forensic tools can detect whether the block was generated by a CFA interpolation process. Authentic blocks exhibit a predictable null space; tampered blocks, having lost this algebraic constraint, show a full-rank covariance structure.

When a forged region is pasted into a host image, it often undergoes geometric transformations like scaling or rotation to fit the scene. These operations leave behind their own periodic interpolation artifacts, distinct from the native CFA pattern. CFA detection algorithms can simultaneously identify the camera's demosaicing signature and any secondary resampling fingerprints. The coexistence of two incompatible interpolation patterns in a single region is a definitive forensic marker of tampering.

A source camera identification method that extracts the unique, stable sensor pattern noise caused by silicon manufacturing imperfections. Every camera sensor leaves a deterministic, high-frequency noise fingerprint on every image it captures. If a region of an image lacks this fingerprint or shows a different PRNU pattern, it indicates splicing from a foreign source. Unlike CFA analysis, which detects interpolation anomalies, PRNU directly identifies the provenance of pixel data at the sensor level.

A forensic method that re-saves an image at a known JPEG quality level and computes the difference from the original. Regions with different compression histories exhibit distinct error levels, revealing potential splicing boundaries. Key characteristics:

• Uniform error levels suggest an untouched image
• High-error regions surrounded by low-error areas indicate inserted content
• Edge discontinuities in the error map highlight cut-and-paste operations

ELA is computationally lightweight but less robust than CFA detection against sophisticated post-processing.

Identifies the statistical fingerprints left when a JPEG image is decompressed, manipulated, and re-compressed. The technique detects misalignment between primary and secondary quantization tables by analyzing:

• DCT coefficient histograms for periodic artifacts
• Blocking artifact inconsistencies at 8×8 pixel grid boundaries
• Quantization table estimation to reveal the original compression parameters

This is particularly effective when a spliced region was originally compressed at a different quality level than the host image.

A deep learning-based technique that extracts a camera model fingerprint capturing local relationships between noise residuals and image semantics. Unlike PRNU, which identifies a specific device, Noiseprint characterizes the processing pipeline of a camera model. The extracted print can:

• Localize tampering at pixel granularity
• Identify inconsistencies between image regions and their claimed source device
• Detect inpainting by revealing absent or anomalous noise patterns

This method complements CFA interpolation detection by analyzing noise rather than demosaicing artifacts.

A blind forensics technique that identifies duplicated regions within the same image by searching for statistically similar pixel blocks. The process involves:

• Block-based matching using DCT, PCA, or keypoint features
• Geometric transformation invariance to detect rotated or scaled copies
• Post-processing verification to filter false matches from natural textures

While CFA detection identifies interpolation anomalies, copy-move detection specifically targets cloning attacks where an attacker conceals objects by duplicating background regions from the same image.

Detects boundaries where a donor region from a foreign image has been inserted into a host image by analyzing local noise statistics. The technique operates on the principle that different images exhibit distinct noise characteristics due to:

• Sensor noise profiles varying between camera models
• ISO-dependent noise patterns that differ across capture conditions
• Post-processing noise introduced by different editing pipelines

When combined with CFA interpolation detection, noise inconsistency analysis provides a multi-modal verification that is significantly harder for forgers to defeat simultaneously.