Untraceable DeepFakes via Traceable Fingerprint Elimination

Jiewei Lai, Lan Zhang, Chen Tang, Pengcheng Sun, Xinming Wang, YUNHAO WANG

Keywords: DeepFakes Attribution;Adversarial Attack;Generative Model Fingerprint

Abstract:

Recent advancements in DeepFakes attribution technologies have significantly enhanced forensic capabilities, enabling the extraction of traces left by generative models (GMs) in images, making DeepFakes traceable back to their source GMs. Meanwhile, several attacks have attempted to evade attribution models (AMs) for exploring their limitations, calling for more robust AMs.
However, existing attacks fail to eliminate GMs' traces, thus can be mitigated by defensive measures.
In this paper, we identify that untraceable DeepFakes can be achieved through a multiplicative attack, which can fundamentally eliminate GMs' traces.
Therefore, by leveraging the structural prior from content-coupled fingerprints, we design a multiplicative attack framework that instills an explicit inductive bias into the adversarial model, guiding it to eliminate fingerprints within DeepFakes, thereby evading AMs even enhanced with defensive measures. This framework trains the adversarial model solely using real data, applicable for various GMs and agnostic to AMs.
Experimental results demonstrate the outstanding attack capability and universal applicability of our method, achieving an average attack success rate (ASR) of 97.08\% against 6 advanced AMs across 12 GMs.
Even in the presence of defensive mechanisms, our method maintains an ASR exceeding 72.39\%.
Our work underscores the potential challenges posed by multiplicative attacks and highlights the need for more robust AMs.

Primary Area: other topics in machine learning (i.e., none of the above)

Submission Number: 8876