## 4 Comparative Results
### Steganalysis Test  
This section discusses the security level of the proposed hiding schemes. In the context of steganography, a hiding scheme is deemed compromised if a third party can reveal the existence of the secret data. The process of detecting hidden data within a carrier is known as steganalysis. The primary goal of a steganalysis tool is to distinguish between the host and carrier signals. Steganalysis techniques are generally classified into three categories: statistical-based methods, signature-based methods, and machine learning methods. Statistical-based methods analyze the statistical properties of the carrier signal to identify hidden data. In contrast, signature-based methods focus on identifying repetitive patterns within the carrier signal that are a result of the steganography technique. Machine learning methods, on the other hand, learn to classify host and carrier signals by being trained on a set of handcrafted features. Recently, with the rise of deep learning techniques such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), these methods have also been employed for steganalysis. The main advantage of deep learning is its ability to automatically extract features from the training data, eliminating the need for manual feature engineering. The RNNs are a class of neural networks designed to process sequential data, making them particularly effective for capturing time-based signals. Unlike traditional feedforward networks, RNNs maintain a hidden state that retains information from previous time steps, allowing them to learn temporal dependencies and patterns within the data. This capability enables RNNs to analyze sequences, such as time series data, natural language, or audio signals, by effectively remembering past inputs while incorporating new information as it becomes available. Therefore, in this work, a long short-term memory (LSTM) network, which is an RNN-based network, is trained to detect and classify temporal signals to be either a host signal or carrier signal. Since a video segment is a spatiotemporal signal, the input data fed into the LSTM network are the features of each frame obtained by a well-known CNN network named GoogLeNet [[https://www.mathworks.com/help/deeplearning/ref/googlenet.html](https://www.mathworks.com/help/deeplearning/ref/googlenet.html)]([https://www.mathworks.com/help/deeplearning/ref/googlenet.html](https://www.mathworks.com/help/deeplearning/ref/googlenet.html)) (accessed on 19 December 2024). Figure 9: Illustrating the effect of tuning the scaling factor \(k\) on the quality of the extracted image and the quality of the carrier video segment. Tests were performed using the proposed Octree-hiding technique when hiding using the Documentary host video segment. The neural network architecture is specifically engineered to process sequence data, comprising several distinct layers each serving a crucial function. The first layer is a sequence input layer designed to handle input with 'n' dimensions, where 'n' represents the number of samples in the input signal. This allows the network to process varying lengths of sequence data effectively. Following the input layer, the network features a bidirectional long short-term memory (BiLSTM) layer, which is configured with 2000 hidden units. The BiLSTM layer is pivotal for the model as it processes data from both forward and backward directions, capturing dependencies and patterns that span across time in the sequence data. This dual-direction processing significantly enhances the network's ability to understand and predict based on the temporal context inherent in the input sequences. Subsequent to the BiLSTM layer, the architecture includes two fully connected layers that serve to synthesize the features extracted by the BiLSTM, facilitating the transition from feature extraction to decision-making processes within the network. These layers are crucial for integrating learned temporal features and preparing the network output for classification.