Abstract: When compared to techniques that work in the spatial domain, frequency domain approaches like the Discrete Cosine Transform (DCT) and the Discrete Wavelet Transform (DWT) are much more accurate. Because of this, both DCT and DWT were tested on public datasets to see how well they could hide pictures. After several tests have been done on the datasets in question, the algorithms are judged based on the Peak Signal-to-Noise Ratio (PSNR) metrics that they have been given. After the information was hidden inside the image, the findings showed that the new stego image had a high degree of not being seen and was also very strong. The DWT method works better than the DCT method, and the images that it creates are much less likely to be ruined by noise. Each pixel in the cover image is given a two-dimensional discrete wavelet transform (DCT) using the discrete wavelet transform (DWT) and DCT algorithms. The secret will be encoded using the DCT coefficient, and it will be decoded using the inverse of the 2D DCT. Because of this, these methods of image steganography can be used to send private information in a wide variety of situations. Deep learning-based techniques for hiding data could make private conversations much harder to find and safer in the near future.

Keywords: Discrete Cosine Transform, Discrete Wavelet Transform, Signal-to-Noise Ratio.