# An Ensemble Transfer Learning Model for Detecting Stego Images
## 4 Results and Analysis  
This study performed tests on a system with two Intel Xeon Silver processors, 256 GB RAM (though only 64 GB was used during training), and two Tesla V100 Ss for the PCIe with double-precision 8.2 teraFLOPS cores. The fully linked convolutional layer weight decays were 0 and 0.01. The linearly decaying learning rate was 0.01-0.00001. The standard BOSS base 1.01 picture dataset [23] was used for the tests. There are 10,000 512 \(\times\) 512 grayscale cover images in BOSS base 1.01. Each image in the collection is broken into four smaller images, each of which is 256 pixels on the longest side. As part of the training process, the authors used a set of four sub-images (one for each image in the training set). Consequently, there were 40,000 images available for training for each DL-based classification modality for the steganography detection. Each dataset, totaling 40,000 individual images, was split in half, with each half containing 20,000 individual pictures. Each image steganography approach resulted in the exposure of half of the training data to the method, with the other half being used for the clear image for training. Each scenario was selected to expose the dataset to a unique steganography method, with a unique payload being expressed in bits per pixel (bpp). This article discusses the edge-adaptive and HUGO steganography methods, which can conceal payloads of 0.2, 0.3, and 0.4 bpp. The dataset was used to train a distinct DL-based classification modality for image steganography detection and contained 20,000 stego images, together with the first half of the 20,000 clean photos. In the context of multimodal DL-based image steganography detection, this amounted to a four-pronged approach. Standard measures were used to evaluate the quality of the proposed models. The first was accuracy, which measures how accurately the dataset as a whole has been labeled. The second statistic, \"precision\", measures how close a classifier comes to correctly labeling a set of outcomes [24; 25; 26; 27; 28; 29; 30]. The percentage of a class's results that are detected by a  
Figure 7: Diagram of an Inception model. model is measured by a metric called recall. An F1 score, which is the harmonic mean of the precision and the recall, was the fourth statistic we considered. Lastly, we compared our method to other current DL-based methods for steganography identification using detection error rates as our metric. Table 1 shows the accuracy of each model based on the ppb. As shown in Table 1, the CNN model achieved a better result than did other models as it had a lower error detection rate. Table 1 is an evaluation metric for error detection in steganography, which is the practice of hiding information within another piece of data. This table compares the performance of various methods for detecting errors in steganography when different levels of payload (information to be hidden) are used. The table lists different methods for error detection (CNN, AlexNet, ResNet-50, Inception, VGG16 Stego, and ANN). For each method, the table shows the error detection rates (EDRs) for different payload levels (0.1, 0.2, 0.3, and 0.4 bits per pixel (bpp)). The EDR is a measure of the effectiveness of a method at detecting errors introduced during the process of steganography. A higher EDR indicates that the method is better at detecting errors while a lower EDR indicates that the method is less effective at detecting errors. All of the methods listed in the table are different types of neural networks that are commonly used in steganography for error detection. CNN, AlexNet, ResNet-50, and Inception are types of convolutional neural networks while VGG16 Stego and ANN are different types of artificial neural networks. The results in the table show that all of the methods had higher EDRs when the payload was set to a lower level (0.1 or 0.2 bpp) and lower EDRs when the payload was set to a higher level (0.3 or 0.4 bpp).