论文标题
任意大小的图像培训和残留内核学习：朝向图像欺诈标识
Arbitrary-sized Image Training and Residual Kernel Learning: Towards Image Fraud Identification
论文作者
论文摘要
在图像中保留原始噪声残差对于图像欺诈标识至关重要。由于深度学习过程中的调整操作会损害图像噪声残差的微观结构，因此我们提出了一个直接训练原始输入量表的框架，而无需调整大小。我们的任意大小的图像训练方法主要取决于伪批次梯度下降（PBGD），该梯度下降（PBGD）弥合了输入批处理和更新批次之间的差距，以确保模型更新通常可以用于任意尺寸的图像。 此外，三相替代训练策略旨在学习最佳的剩余内核，以识别图像欺诈识别。借助学习的残留核和PBGD，提出的框架实现了最先进的结果，从而导致了图像欺诈识别，尤其是对于具有较小的篡改区域的图像或具有不同篡改分布的看不见的图像。
Preserving original noise residuals in images are critical to image fraud identification. Since the resizing operation during deep learning will damage the microstructures of image noise residuals, we propose a framework for directly training images of original input scales without resizing. Our arbitrary-sized image training method mainly depends on the pseudo-batch gradient descent (PBGD), which bridges the gap between the input batch and the update batch to assure that model updates can normally run for arbitrary-sized images. In addition, a 3-phase alternate training strategy is designed to learn optimal residual kernels for image fraud identification. With the learnt residual kernels and PBGD, the proposed framework achieved the state-of-the-art results in image fraud identification, especially for images with small tampered regions or unseen images with different tampering distributions.
