<![CDATA[The growing reliance on digital video across education, entertainment, communication, and security applications has made video one of the most widely used forms of multimedia data, yet this growth has also exposed video files to risks such as unauthorized access, data theft, content manipulation, and misuse of digital information. This study addresses these risks by implementing the Discrete Wavelet Transform (DWT) algorithm as a signal-processing-based method for securing video files. DWT decomposes each video frame from the time domain into the frequency domain, producing a visual representation that is difficult to recognize, while the Inverse Discrete Wavelet Transform (IDWT) reconstructs the video back to a form approximating the original. A security key was applied to the wavelet coefficients to prevent reconstruction without proper authorization. The system was built in Python using OpenCV for video processing, PyWavelets for the DWT/IDWT operations, NumPy for numerical computation, and CustomTkinter for the desktop interface, following a Research and Development approach with a Waterfall development model. Black box testing confirmed that the encryption and decryption functions operated correctly across the tested video files, and quality evaluation using Peak Signal to Noise Ratio (PSNR), Mean Squared Error (MSE), Signal to Noise Ratio (SNR), and entropy showed that the encrypted videos exhibited substantial visual distortion relative to the originals, with PSNR values around 10–11 dB, MSE values in the range of roughly 5,100–5,700, and an entropy of approximately 7.37, indicating that the visual structure of the video content was concealed effectively. These findings suggest that DWT can serve as a practical signal-transformation-based approach to video data security, although its protection level remains below that of dedicated cryptographic algorithms such as AES or RSA, leaving room for future work that combines DWT with stronger cryptographic techniques.]]>
