  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.
