A Hybrid Security System for Text Encryption and Steganography in Video Using Multi-Level Chaotic Maps
Bibliographic record
Abstract
The swift advancement of information and communication technology has made it increasingly difficult to guarantee the security of transmitted data.Traditional encryption techniques, particularly in multimedia applications, frequently fail to defend against sophisticated attackers.By combining multi-level chaotic maps with Least Significant Bit (LSB) steganography and Advanced Encryption Standard (AES) encryption, this study proposes an improved security approach for text transmission.Multiple well-known chaotic maps integrate into the chaotic system to guarantee randomness as well as key unpredictability through the Arnold Cat Map, Ikeda Map, Tent Map, Henon Map, Gingerbread Man Map, Standard Map, and Zaslavsky Map.A hybrid chaotic system dynamically creates the encryption keys, guaranteeing high unpredictability and resistance to brute-force attacks.Next, it incorporates the encrypted text into video frames, making it challenging to find the secret data.The suggested method executes three fundamental steps, which start with chaotic system-based dynamic key genesis, followed by AES encryption enabled by the generated key, and culminating in LSB steganographic text embedding insertion.The suggested method demonstrates its resilience to statistical attacks by passing 13 out of 16 NIST randomness tests and achieving high entropy values above 7.98, along with strong Chi-Square statistics confirming uniformity of encrypted text distribution.Our hybrid approach improves data secrecy and resistance to various cryptographic attacks.The proposed system provides superior encryption capabilities together with better randomness, and withstands statistical attacks while maintaining while preserving the imperceptibility of the video content .Experimental results confirm the efficiency of the suggested technique in safely sending sensitive textual information while preserving the video content's imperceptibility.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".