Image Forgery Detection Techniques and Forensic Analysis

Digital images have become ubiquitous in journalism, legal evidence and social media, yet their ease of manipulation threatens trust across multiple domains. Forgery detection and forensic analysis encompass a variety of passive and active approaches aimed at establishing image authenticity and localising tampered regions. Passive techniques exploit intrinsic traces left by imaging devices, such as sensor pattern noise or lens distortions, and statistical inconsistencies introduced by manipulations. Active methods embed watermarks or digital signatures at capture time, but their adoption remains limited. Common forgery classes include copy-move (duplicating regions within an image), splicing (combining elements from different images), double JPEG compression, recapture from screens and emerging deepfake content. Recent advances leverage handcrafted feature descriptors—such as local binary patterns and discrete cosine transform coefficients—and increasingly employ deep learning models to capture subtle residual patterns that elude human perception. Convolutional neural networks trained on large, realistic datasets can both detect and localise multiple forms of tampering, while specialised pipelines address challenges posed by compression artefacts, scaling, rotation and social-media re-encoding. The global significance of robust image forensics spans criminal investigations, media verification and the safeguarding of scientific integrity.

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Image Forgery Detection Techniques and Forensic Analysis publication trend

The graph below shows the total number of articles in image forgery detection techniques and forensic analysis across all publications each year (not limited to Nature Index journals).

Technical terms

Copy-move forgery: A manipulation technique in which a region of an image is duplicated elsewhere within the same image to conceal or replicate content.

Image splicing: The process of combining portions of two or more images to create a composite that may appear seamless.

Convolutional neural network (CNN): A deep learning architecture that applies learnable filters to extract hierarchical features from image data, widely used for classification and localisation tasks.

Discrete cosine transform (DCT): A mathematical transformation that expresses image data in terms of frequency components, commonly used in compression and forensic analysis of JPEG images.

Sensor pattern noise (SPN): A unique noise fingerprint inherent to each digital imaging sensor, used for source camera identification and tamper detection.

Forgery localisation: The identification and mapping of specific regions within an image that have been manipulated or tampered with.

References

1. Copy-Move Forgery Verification in Images Using Local Feature Extractors and Optimized Classifiers. Big Data Mining and Analytics (2023).
2. Double JPEG compression forensics based on a convolutional neural network. EURASIP Journal on Information Security (2016).
3. Deep Learning Local Descriptor for Image Splicing Detection and Localization. IEEE Access (2020).
4. Automatic source camera identification using the intrinsic lens radial distortion.. Optics Express (2006).
5. A context-adaptive SPN predictor for trustworthy source camera identification. EURASIP Journal on Image and Video Processing (2014).