Applying Fibonacci requires us to utilize secondary cryptographic hashes, such as SHA-256 or 512 to confirm detection once flagged by the Fibonacci. What this facilitates is the use of stronger hashes while improving performance. Since the Fibonacci hash acts as a filter to eliminate files that are not in the Known Hash Set this dramatically reduces the number SHA-256 or 512 that must be generated resulting in improved performance.
Advanced JPEG Artifact Detection:
The JPEG steganography detection module was developed
as part of the TAPS project, and utilizes machine learning techniques to unearth traits from jpeg images in order to detect the presence of hidden data in JPEG images. JPEG is a widely used format in image steganography due to its huge popularity as a multimedia carrier.
The above diagram depicts the improved performance
resulting in the use of Fibonacci Hashing.