# Advancing Image Spam Detection: Evaluating Machine Learning Models Through Comparative Analysis
## 2 Related Work
Another significant contribution to image spam detection comes from [10], in which a new dataset of spam images was introduced, which were challenging to detect with existing methods. The authors found that both PCA and SVM models achieved high accuracy with low computational complexity. More research in spam detection uses deep learning model techniques. In the research conducted in [11], the authors showed that deep learning models such as Long Short-Term Memory (LSTM) and convolutional neural networks (CNNs) can surpass traditional ML methods by automatically extracting features. Additionally, ref. [12] offers an even more accurate alternative compared to methods like Optical Character Recognition and SVM, emphasizing the evolving role of advanced ML algorithms in countering image and email spam. Regarding the image spam filtering methods, in [13], the authors introduced a deep learning-based approach by utilizing pre-trained convolutional neural networks (CNNs), including InceptionV3, DenseNet121, ResNet50, VGG16, and MobileNetV2. Their framework leveraged transfer learning, data augmentation, and replaced the fully connected layers with a Support Vector Machine (SVM) classifier, significantly improving accuracy and computational efficiency. A study found that the SVM performed exceptionally well in detecting image spam, overcoming traditional methods due to its ability to learn from visual data and adapt to various image variations [14]. Experiments conducted on standard datasets demonstrated that ResNet50 was the best-performing model, achieving an accuracy of 99.87%. Despite impressive accuracy, the high resource demand of these deep learning models can be a barrier for real-time applications. Looking for a suitable ML technique for image spam detection, XGBoost has gained attention for its ensemble learning technique, which merges the output of multiple decision trees to improve the overall prediction accuracy. However, it is not easy to reasonably tune the hyperparameters. To obtain greater accuracy from XGBoost, it requires prior knowledge of researchers and their experience in parameter tuning, as well as a great deal of time [15]. Lastly, SVM has also been used in spam classification tasks due to its strong handling of high-dimensional data [16]. Several studies have proven that SVM can classify images when combined with feature extraction techniques. However, SVM models require extensive tuning of their parameters in complex image datasets. These two models have allowed for adaptive learning, so they can continuously improve accuracy by adapting to new types of spam images. This is crucial given the constantly evolving tactics of spammers [17]. However, studies considering XGBoost and SVM rarely offer comparative benchmarks against deep learning models on the same dataset. To address the gaps identified in prior research, including inconsistent benchmarking, limited comparative evaluation, and less processing power utilization, our study experimented on multiple machine learning models under identical conditions to determine which model achieves higher accuracy and fewer false positives.