Data security and privacy concerns arise from sharing sensitive data online. Methods such as digital watermarking, cryptography, and steganography are employed to protect data. Steganography excels beyond alternative methods in safeguarding data against potential threats, with superior effectiveness and discretion. It is a promising method for safely transmitting private data across an insecure channel that conceals information from viewing. The study of hiding secret information inside an image using several methods is known as image steganography. One of the main problems with image steganography techniques is their imperceptibility and large embedding capacity. In this study, a comparative practical performance analysis of three image steganography techniques (k-LSB, CAIS, and HiNet) is performed on three different datasets (DIV2K, ImageNet, and COCO) and evaluated using four different performance metrics: SSIM, PSNR, RMSE, and MAE. The experimental results revealed that HiNet consistently achieved the best performance across all metrics and datasets. On the DIV2K dataset, HiNet achieves a PSNR of 46.57 dB and an SSIM of 0.993, significantly outperforming 4bit-LSB and CAIS. Similarly, on the ImageNet and COCO datasets, HiNet demonstrates superior performance with PSNR values of 36.63 dB and 36.55 dB, and SSIM values of 0.960 and 0.961, respectively. These results indicate that HiNet, an invertible neural-network-based method, provides substantial improvements in both the concealing and revealing of secret images, making it a highly effective solution for image steganography.