Image manipulation localization is one of the most challenging tasks because it pays more attention to tampering artifacts than to image content, which suggests that richer features need to be learned. Unlike many existing solutions, we employ a semantic segmentation network, named Multi-Supervised Encoder–Decoder (MSED), for the detection and localization of forgery images with arbitrary sizes and multiple types of manipulations without extra pre-training. In the basic encoder–decoder framework, the former encodes multi-scale contextual information by atrous convolution at multiple rates, while the latter captures sharper object boundaries by applying upsampling to gradually recover the spatial information. The additional multi-supervised module is designed to guide the training process by multiply adopting pixel-wise Binary Cross-Entropy (BCE) loss after the encoder and each upsampling. Experiments on four standard image manipulation datasets demonstrate that our MSED network achieves state-of-the-art performance compared to alternative baselines.

中文翻译：

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用于图像伪造定位的多监督编码器-解码器

图像操作定位是最具挑战性的任务之一，因为它更关注篡改伪影而不是图像内容，这表明需要学习更丰富的特征。与许多现有解决方案不同，我们采用语义分割网络，称为多监督编码器 - 解码器（MSED），用于检测和定位具有任意大小和多种类型操作的伪造图像，而无需额外的预训练。在基本的编码器-解码器框架中，前者通过多速率的空洞卷积对多尺度上下文信息进行编码，而后者通过应用上采样逐渐恢复空间信息来捕获更清晰的对象边界。额外的多监督模块旨在通过在编码器和每次上采样后乘以像素级二进制交叉熵 (BCE) 损失来指导训练过程。在四个标准图像处理数据集上的实验表明，与替代基线相比，我们的 MSED 网络实现了最先进的性能。