Securing Medical Images Using Chaotic Map Encryption and LSB Steganography
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".