Image manipulation detection algorithms designed to identify local anomalies
often rely on the manipulated regions being ``sufficiently'' different from the
rest of the non-tampered regions in the image. However, such anomalies might
not be easily identifiable in high-quality manipulations, and their use is
often based on the assumption that certain image phenomena are associated with
the use of specific editing tools. This makes the task of manipulation
detection hard in and of itself, with state-of-the-art detectors only being
able to detect a limited number of manipulation types. More importantly, in
cases where the anomaly assumption does not hold, the detection of false
positives in otherwise non-manipulated images becomes a serious problem.
To understand the current state of manipulation detection, we present an
in-depth analysis of deep learning-based and learning-free methods, assessing
their performance on different benchmark datasets containing tampered and
non-tampered samples. We provide a comprehensive study of their suitability for
detecting different manipulations as well as their robustness when presented
with non-tampered data. Furthermore, we propose a novel deep learning-based
pre-processing technique that accentuates the anomalies present in manipulated
regions to make them more identifiable by a variety of manipulation detection
methods. To this end, we introduce an anomaly enhancement loss that, when used
with a residual architecture, improves the performance of different detection
algorithms with a minimal introduction of false positives on the
non-manipulated data.
Lastly, we introduce an open-source manipulation detection toolkit comprising
a number of standard detection algorithms.

本研究通过深度学习和基于学习的方法，对图像篡改检测进行了深入分析，在标准数据集上评估了它们的性能；提出一种新颖的基于深度学习的预处理技术，用于强调篡改区域中的异常，通过加强篡改区域中的异常，提高了不同检测算法的性能，降低了在非篡改区域中的误检率，最后提供一个开源的篡改检测工具包。