论文标题
有效的图像篡改本地化，多尺度convnext特征融合
Effective Image Tampering Localization with Multi-Scale ConvNeXt Feature Fusion
论文作者
论文摘要
随着强大图像编辑工具的广泛使用，图像篡改变得容易且现实。现有的图像法医方法仍然面临低概括性能和鲁棒性的挑战。在这封信中，我们提出了一种基于Convnext网络和多尺度特征融合的有效图像篡改本地化方案。堆叠的Convnext块用作捕获层次多尺度特征的编码器，然后将其融合在解码器中，以准确定位篡改像素。采用联合损失和有效的数据扩展以进一步提高模型性能。广泛的实验结果表明，我们提出的计划的本地化性能优于其他最先进的方案。源代码将在https://github.com/zhuhc98/itl-ssn上找到。
With the widespread use of powerful image editing tools, image tampering becomes easy and realistic. Existing image forensic methods still face challenges of low generalization performance and robustness. In this letter, we propose an effective image tampering localization scheme based on ConvNeXt network and multi-scale feature fusion. Stacked ConvNeXt blocks are used as an encoder to capture hierarchical multi-scale features, which are then fused in decoder for locating tampered pixels accurately. Combined loss and effective data augmentation are adopted to further improve the model performance. Extensive experimental results show that localization performance of our proposed scheme outperforms other state-of-the-art ones. The source code will be available at https://github.com/ZhuHC98/ITL-SSN.
