Banknote Recognition and Counterfeit Detection Systems

Banknote recognition and counterfeit detection systems combine optical, machine‐learning and sensor technologies to ensure the authenticity and fitness of paper currency. Traditional security features such as watermarks, micro‐printing, raised intaglio and holographic strips are increasingly complemented by advanced imaging modalities—including ultraviolet and near‐infrared scanning—to reveal hidden inks and fibres. Automated systems employ cameras or specialised sensors to capture multispectral images, which are analysed by algorithms to classify denomination, detect forgeries and assess physical wear. Recent advances harness deep learning to improve robustness against variable lighting and note degradation, while the integration of unique fluorescent or fibre‐based identifiers enables per‐note traceability. Applications range from banknote sorting machines in central banks and cash counters in retail to smartphone‐based tools for visually impaired users and portable validators for field inspections. As cash circulation persists worldwide, these technologies play a vital role in reducing fraud, streamlining cash handling and maintaining trust in monetary systems.

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Banknote Recognition and Counterfeit Detection Systems publication trend

The graph below shows the total number of articles in banknote recognition and counterfeit detection systems across all publications each year (not limited to Nature Index journals).

Technical terms

Security feature: A deliberate design element—such as a watermark or hologram—added to banknotes to deter and reveal counterfeiting.

Fluorescent security feature: Substances or fibres that emit visible light when illuminated by ultraviolet radiation, used for unique note identification.

Convolutional neural network (CNN): A deep learning architecture optimised for image recognition, employing convolutional layers to detect patterns and features.

Transfer learning: A technique in which a pre-trained model is adapted to a new task by fine-tuning its parameters on fresh data.

References

1. Banknote Identification Through Unique Fluorescent Properties. IEEE Transactions on Dependable and Secure Computing (2023).
2. A Survey on Banknote Recognition Methods by Various Sensors. Sensors (2017).
3. Deep Learning-Based Fake-Banknote Detection for the Visually Impaired People Using Visible-Light Images Captured by Smartphone Cameras. IEEE Access (2020).