The 21st century might be considered the "boom" period for social networking due to the fast expansion of social media use. In terms of user privacy and security regulations, a plethora of new requirements, issues, and concerns have arisen due to the proliferation of social media. With the increase in social media use, images on social media are often modified or fabricated for certain purposes. Therefore, this work implements and evaluates the SPIRAL-LSB algorithm for common attacks for social media images. Image compression was also discussed as images published to social media platforms was often compressed. An analysis was performed to assess the algorithm's output on social media images. The experiments were carried out prior to and after uploading to the Instagram platform. The dataset was subjected to image splicing, copy-move, cut-andpaste, text insertion, and 3D-sticker insertion attacks. The outcome of SPIRAL-LSB was effective for text insertion attacks solely. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) were selected as the experiment's metrics. The average PSNR value is 63.25, and the SSIM value is 0.99964, both of which are regarded high. This indicates that the watermark has not degraded the quality of the images. This work was designed for usage on social media for intellectual property reasons and may be used to validate the validity of social media images and prevent issues with image integrity, such as image manipulation.