<![CDATA[The rapid expansion of Internet of Things (IoT) edge systems has increased the demand for image steganography methods that provide strong security, high visual quality, and low computational overhead under resource-constrained environments. Existing adaptive steganography techniques, such as HUGO, WOW, and S-UNIWARD, achieve high embedding performance but generally rely on computationally intensive optimization procedures that limit their applicability to real-time IoT edge devices. This study addresses this limitation by proposing a Deterministic Texture-Adaptive Steganography framework based on a Texture Complexity Analyzer (TCA) integrated with Syndrome-Trellis Codes (STC). The TCA module generates a deterministic pixel-wise distortion cost map using second-order directional filtering, while the STC encoder performs minimum-distortion message embedding through trellis-based optimization with linear computational complexity. The proposed framework was evaluated on 40,000 images from the LIRMM CroppedBOSSbase dataset using five-fold stratified cross-validation. Experimental results demonstrate a mean PSNR of 59.63 dB, an SSIM of 0.9996, and a detection error of 0.50 against SR-Net steganalysis, indicating high visual imperceptibility and strong statistical security. The framework also achieved an average execution time of 80 ms per 256 × 256 image with an estimated energy consumption of 0.28 J, substantially reducing computational cost compared with iterative baseline methods. These findings demonstrate that the proposed deterministic framework provides an efficient balance between security, imperceptibility, and computational efficiency, making it well suited for practical deployment in resource-constrained IoT edge environments where predictable execution time and energy efficiency are essential.]]>
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