With the widespread use of powerful image editing tools, image tampering becomes easy and realistic. Existing image forensic methods still face challenges of low accuracy and robustness. Note that the tampered regions are typically semantic objects, in this letter we propose an effective image tampering localization scheme based on deep semantic segmentation network. ConvNeXt network is used as an encoder to learn better feature representation. The multi-scale features are then fused by Upernet decoder for achieving better locating capability. Combined loss and effective data augmentation are adopted to ensure effective model training. Extensive experimental results confirm that localization performance of our proposed scheme outperforms other state-of-the-art ones.

翻译：随着强大的图像编辑工具的广泛使用,图像篡改变得容易和现实。现有的图像法证方法仍然面临着低精确度和稳健度的挑战。请注意,被篡改的区域通常是语义物体,我们在此信中提议一个基于深层语义分割网的有效图像篡改本地化计划。ConvNeXt网络被用作一个编码器,以学习更好的特征描述。然后,由Upernet解码器将多尺度的功能结合起来,以获得更好的定位能力。采用了合并损失和有效的数据增强,以确保有效的示范培训。广泛的实验结果证实,我们拟议计划的本地化业绩优于其他最先进的系统。