Fingerprints are a unique way of identifying an individual, as no two individuals have the same fingerprint. The fingerprint doesn’t lose its pattern even under adverse conditions where the fingers might undergo burns or tears of the skin. So, fingerprint biometrics is considered more reliable compared to other biometrics. Therefore, it is widely used and will benefit us in the future if we overcome its current threats. Some menaces are that artificial fingers made of clay, mouldable plastic, play-doh, gelatine, rubber, and silicone can spoof fingerprint scanners. Thus, developing an efficient method to protect fingerprint systems from imposter access is the day’s need to ensure that only real fingerprints should be used for authentication and enrolment. Thus, liveness detection can be used as it detects physiological features of life from fingerprints. The proposed method adopts FDeblur-GAN, then removes the blurriness from the fingerprint image, and this De-Blurred image is fed to deep learning models such as CNN, and VGG-16, which then identifies if the fingerprint image is live or fake. Furthermore, it identifies the type of spoofing material used when the sample is fake.