A Comprehensive Research of Image Tampering Detection Techniques Based on Deep Learning
Bosheng Yang · Advances in engineering research/Advances in Engineering Research · 2024
This paper reviews the advancements in image tampering detection technologies driven by deep learning.Traditional methods, dependent on manually crafted features, often fall short when confronting sophisticated tampering techniques.In contrast, deep learning models such as Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), and Recurrent Neural Networks (RNNs) significantly enhance detection capabilities through robust feature extraction and pattern recognition.These models excel in various aspects of detection: CNNs in spatial analysis, GANs in improving robustness via adversarial training, and RNNs in capturing temporal sequences in data.Despite facing challenges like the necessity for extensive annotated datasets and issues with interpretability, ongoing research is dedicated to refining these models for better generalization and efficiency.Ongoing research is enhancing model efficiency and generalization, with future work focusing on integrating multimodal data and developing more interpretable deep-learning models to ensure the integrity of visual content.Future directions aim to expand upon current capabilities by integrating multimodal data and developing models that are easier to interpret, thus ensuring the integrity and authenticity of visual content across various digital platforms.