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Yongxin Xue

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Proceedings Article | 30 November 2022 Paper

ISD-SSD: image splicing detection by using modified single shot MultiBox detector

Yongxin Xue, Chen Zhu, Xiangqiong Tan

Proceedings Volume 12456, 124562J (2022) https://doi.org/10.1117/12.2659381

KEYWORDS: RGB color model, Detection and tracking algorithms, Image fusion, Feature extraction, Performance modeling, Modulation, Convolution, Target detection, Image forensics, Color image processing

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Detecting image splicing has become essential to fight against malicious forgery. To solve the problems of low accuracy of authenticity classification and low detection efficiency in some image splicing detection methods proposed in recent years, we propose an image splicing forgery detection method based on modified SSD network (ISD-SSD). In this method, the residual network model ResNet-50 is used to replace the feature extraction backbone network VGG-16 in SSD, which solves the problem of degradation caused by the deepening of the network and enhances the feature extraction ability of the model. In the multi-scale detection part, a multi-scale feature fusion module is introduced based on the feature pyramid idea, which organically combines the low-level visual features and high-level semantic features in the network structure. Finally, Focal loss is selected as the loss function in the loss calculation part to solve the problem of positive and negative samples and the imbalance of difficult and easy samples. Experiments on standard image manipulation datasets demonstrate that our ISD-SSD algorithm is superior to the existing image tamper detection algorithms (such as MFCN, Faster R-CNN algorithm, etc.), the evaluation metrics AP and F1 reach 77.86% and 75.18% respectively. In addition, it shows robustness in terms of detection speed.

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