Steganography Detection using Convolutional Neural Networks: A Deep Learning Approach

Steganography is a method of concealing any message or information within digital images, making it challenging to detect hidden messages. This study focuses on developing a deep learning-based approach for the detection of steganography with the help of Convolutional Neural Networks (CNNs). We implement and compare the performance of LeNet-5 and AlexNet architectures in classifying images as either clean or stego. The dataset comprises images embedded using the Least Significant Bit (LSB) technique. The models are then trained with augmented image data and then evaluated using various accuracy metrics. Experimental results indicate that AlexNet, with its deeper architecture, achieves higher accuracy than LeNet-5 in identifying stego-images. The findings demonstrate the potency of deep learning in automated steganalysis, highlighting the potential for CNNs in cybersecurity applications.