Image Operation Chain Detection with Machine Translation Framework

The aim of operation chain detection for a given manipulated image is to reveal the operations involved and the order in which they were applied, which is significant for image processing and multimedia forensics. Currently, all existing approaches simply treat image operation chain detection as a classification problem and consider only chains of at most two operations. Considering the complex interplay between operations and the exponentially increasing solution space, detecting longer operation chains is extremely challenging. To address this issue, in this work, we devise a new methodology for image operation chain detection. Different from existing approaches based on classification modeling, we strategically conduct operation chain detection within a machine translation framework. Specifically, the chain in our work is modeled as a sentence in a target language, with each possible operation represented by a word in that language. When executing chain detection, we propose first transforming the input image into a sentence in a latent source language from the learned deep features. Then, we propose translating the latent language into the target language within a machine translation framework and finally decoding all operations, arranged in order. Besides, a chain inversion strategy and a bi-directional modeling mechanism are developed to improve the detection performance. We further design a weighted cross-entropy loss to alleviate the problems presented by imbalance among chain lengths and chain categories. Our method can detect operation chains containing up to seven operations and obtains very promising results in various scenarios for the detection of both short and long chains.