Anonymous Image Processing-Chain Forensics by Example

Recovering an image’s processing history is a central problem in digital image forensics. Existing methods recover named operation sequences or compare processing traces. When operation names and configurations are unavailable, a few before-and-after examples provide another source of evidence. We introduce anonymous image processing-chain inference, a new task for ranking candidate histories between two image versions using these examples. Our central finding is that a history decision can be determined before its operations are fully identified. We derive sufficient conditions under which all example-compatible models in a stated parameter neighborhood prefer the same history, accounting for repeated operations and propagated estimation errors. We propose ChainPrint, which estimates reusable operation functions from paired examples within a declared model library and composes them to address diverse and compound history-inference problems without task-specific retraining. Shuffling example pairings degrades recovery; replacing sequential composition with additive operation changes lowers eight-step AI Top-1 accuracy by 41.4 percentage points. Across three image sources, eight-step Top-1 reaches 100.0% for conventional histories and 77.8% for AI-containing histories, with eight examples per operation and sixteen candidates. A fully enumerated two-dimensional control shows that ranking stability depends on which parameter directions the examples constrain, even when example fit and overall operation disagreement are matched. These results focus history recovery on the operation knowledge needed to distinguish competing histories, making example-based inference possible without requiring complete operation identification.