Securing Medical Images Using Chaotic Map Encryption and LSB Steganography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Secure image transfer is a difficult topic in the age of communication technology because millions of people utilize and share images online for personal and professional reasons.Encryption algorithms, such as cipher images, help achieve secure transfer through networks.Despite attackers having decryption keys, they cannot retrieve the original image.To ensure integrity assurance, prevent changes to medical images that could lead to a misdiagnosis, transmit patient medical records in a private and secure manner, and prevent falling victim to cyberattacks, a high-performance, effective method of encrypting medical images must be developed.Encrypting medical images is common in telemedicine, making secure image transfer essential.The medical dataset includes personal data about the health of a patient.All essential information, including medical images, is now kept on picture and communication servers because of the growing interest in inpatient records across the world.In this study, we presented a unique approach to medical picture encryption that combines the Triple data encryption algorithm (3DES) and advanced encryption standard (AES) methods with three chaotic maps (Logistic, Arnold CAT, and Baker).The BAT optimization algorithm is also used to accomplish the task of key generation.Finally, the Least Significant Bit (LBS) is used to hide encrypted medical images before sending them to the server by TCP/IP protocol.The experiments yielded promising results in entropy of 5.92, PSNR of 0.99, and MSE 0.0001.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it