Secure Spread Spectrum Image Steganography Using a CNN-Based Learned Detector

Spread Spectrum Image Steganography (SSIS) represents a promising approach for embedding secret data into a cover image. In conventional methods, a pseudo-noise (PN) sequence functions as a secret key, without which neither message embedding nor data extraction is feasible. However, this secret key presents a potential security risk, as an adversarial service may attempt to uncover it, and if successful, unauthorized access could enable the extraction of the secret data. This study introduces a novel steganography technique inspired by spread spectrum principles. Unlike conventional methods that embed information directly using PN sequences and employ correlator-based detection, the proposed method does not depend on the PNs themselves as the primary carrier. Instead, it constructs structured PN-based patterns and encodes the secret message within these patterns rather than within the raw PNs. As a result, the reliance on PN values is eliminated in the proposed Decoder/Encoder steganographic framework, thereby enhancing the security of proposed approach compared to traditional methods. To extract the secret data, a convolutional neural network (CNN) model is employed to classify the received PN pattern and determine the corresponding pattern class. Experimental results indicate that the proposed CNN-based method is not only competitive with other deep learning-based approaches but also outperforms conventional SSIS techniques under the evaluated attacks. Moreover, as a secondary contribution, the method offers an additional advantage over conventional SSIS techniques by enhancing robustness against some geometric attacks, a well-known limitation of traditional approaches.