A Web-based Least Significant Bit (LSB) Image Steganographic Technique
Bibliographic record
Abstract
Steganography is the art of hiding secret messages into cover during communication. Steganography is a technique for sending secret messages across ordinary cover carriers in such a way that the presence of the messages is unnoticed. There are various steganographic techniques based on the cover being used in the steganographic process. The covers can be an image, audio, video, and text. The most widely used steganographic technique nowadays is Image Steganography. Image steganography is hiding the existence of the data using the image as the cover object. Most of the image steganographic technique hides the secret messages as plaintext and intruders may try to extract the secret message if he/she knows that the image being communicated is a stego image. In this paper, a web-based Least Significant Bit (LSB) image steganographic technique with two layers of AES encryption that does not require a key exchange mechanism between sender and receiver is explained. The proposed method follows six step mechanism on the sender’s side which includes user authentication to initiate communication, two 128-bit key generation and storing it to the central server database, message encryption using one of the generated keys in previous step and image encryption using another key followed by the image steganography. The image generated after sixth step is ready to send via any medium to the receiver. The receiver of the image also follows six steps process to convert the stego image to the decrypted message. After completing the authentication, receiver inputs the received image from the sebder and the system checks for the integrity of the inputted image. Once the integrity is verified, the system pulls the decryption keys from the database. Using this decryption keys, the image decryption and message takes place. The goal of the proposed method is to avoid key exchange mechanism using client/server architecture. The proposed method encrypts the secret message and stego image to add another layer of security.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".