This paper discusses an adaptive method of image steganography issues based on the application of a linear hash function over the GF (2) field to control the embedding process. The method uses staggered splitting of an image into 8 × 8-pixel blocks to provide blind steganography. Classification thresholds are defined as the percentiles of the distribution of gradients throughout the image, allowing for efficient load distribution between textured and smooth areas. Experiments on the BOSSBase, SIPI and Kaggle kits show that the method provides an actual capacity of up to 0.7 bpp at PSNR 47–50 dB and is resistant to statistical tests and RS analysis. At the same time, like other approaches based on modification of pixel differences, it remains vulnerable to modern stegoanalysis based on spatial rich models (SRMs). However, thanks to the modular structure of embedding control based on linear hash function, the proposed architecture allows direct integration with many modern adaptive strategies aimed at minimizing statistical anomalies.

Kazakh vowel harmony is well established linguistically, but its explicit integration into modern neural automatic speech recognition (ASR) remains insufficiently studied. This work investigates whether a productive front/back vowel-harmony constraint can be operationalized within Whisper large-v3 for Kazakh ASR. Two complementary strategies are evaluated on the ISSAI Kazakh Speech Corpus 2: (1) a differentiable token-level harmony regularizer added during fine-tuning and (2) harmony-based N-best hypothesis re-ranking during inference. We further introduce the Harmony Violation Rate (HVR) as a diagnostic measure and analyze Whisper’s byte-level BPE representation of Kazakh vowels. Neither integration strategy produces a statistically significant improvement in test WER. Re-ranking yields a small but statistically significant reduction in HVR, while additional analyses show no recognition disadvantage for byte-fragmented vowels and no systematic reduction in front/back vowel confusions after harmony-constrained fine-tuning. These findings show that simple token-level regularization and sentence-level re-ranking do not adequately model the contextual stem–suffix dependency underlying Kazakh vowel harmony. The study therefore identifies specific limitations of straightforward phonological-constraint integration and provides diagnostic evidence for future representation- and morphology-aware Kazakh ASR.