02920ntm a2200373 i 4500 0 0 lcc 0 1 20000 20000 2023-05-08 0 FSKKP .J56 2019 r Bc. T000002369 2023-05-08 00:00:00 2023-05-08 THESIS 0 0 lcc 0 0 20000 20000 2023-05-08 0 CD13350 T000002370 2023-05-08 00:00:00 2023-05-08 THESIS 99502 99508 MY-KuUP 20251125110737.0 t||||fr|||| 000 0 ta 230508t20192019my a|||fr|||| 000 0 eng d THE0009647 (Local) Hardback UMP eng UMP rda FSKKP .J56 2019 r Bc. Lim Jee Chao, author. Reversible image steganography using roi & roni / Lim Jee Chao Kuantan, Pahang : UMP, 2019 ©2019 xii, 68 pages : rdacontent text rdacontent text rdamedia unmediated rdamedia computer rdacarrier volume rdacarrier computer disc rda text file PDF Faculty of Computer Systems and Software Engineering Project paper (Bachelor of Computer Science (Software Engineering) With Honours -- Universiti Malaysia Pahang - 2015 Bibliography : page 65-66 Reversible steganography allows to recover of original image without any distortion when the embedded secret message has extracted. This research was tested with four different image and one storage image. The sample image is divided into three type of region which are Region of Interest (ROI), Region of Non-Interest (RONI) and untouchable region. This research using the technique of finding the ROI and RONI of the cover image to find position to embed the message and recover the original image. During embedding process, the RONIs’ bits are stored into the storage image known as sample_image. Next, the ROIs’ bits are stored into RONI so that it can be recovered during extraction process. Sender select the x-coordinate and y-coordinate to embed the secret information into the ROI2. Sender also need embed the secret key in the RONI2 image which can help to secure the secret information. After that, stego-image was generated after ROI and RONI embedded. In extraction process, receiver need to extract the secret key to decrypt the secret message. For reversible process, ROIs and RONIs were reversed to original bits. Peak Signal-to-Noise Ratio (PSNR) value was used to measure the quality of stego-image and similarity of original image and recover image. The value of PSNR of the four selected sample image is between 52.60dB to 52.62dB. Histogram of original image, stego-image and recover image are generated for visual difference between original image, stego-image and recover image. In conclusion, this proposed method has proved a better approach compared to previous work in terms of selecting a position to embed by using ROI and RONI. Faculty of Computer Systems and Software Engineering Dissertations Universities and colleges Dissertations Theses Dissertations lcc THESIS