Prateeksha Gaur, Amit Verma and Manish Kumar Sharma, Dr. K. N. Modi University, India

This paper presents a lightweight framework for secure metadata embedding in unmanned aerial vehicle (UAV) communication. An on-board convolutional neural network (CNN) classifier generates image predictions, which are directly embedded into the corresponding image using Least Significant Bit (LSB) steganography. This approach eliminates the need for separate metadata channels, thereby enhancing confidentiality and improving bandwidth efficiency. The proposed framework is validated on a subset of the CIFAR-10 benchmark dataset as well as the UAV-specific VisDrone2019-DET dataset. The predicted class labels and confidence scores are embedded within the images and can be extracted without error. The methodology includes CNN model design, LSB embedding and extraction, and evaluation across classification accuracy and image quality metrics. The evaluation employs accuracy, Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). Experiments show classification accuracies of 33.0% on CIFAR- 10 and 48.36% on VisDrone. Visual quality is preserved, with PSNR values above 63.0 dB and SSIM scores near 1.0.The results confirm that the proposed method achieves secure metadata embedding with negligible visual distortion, offering an efficient and lightweight solution for UAV communication

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