ForensicTransfer: Weakly-supervised Domain Adaptation for Forgery Detection (1812.02510v2)

Abstract: Distinguishing manipulated from real images is becoming increasingly difficult as new sophisticated image forgery approaches come out by the day. Naive classification approaches based on Convolutional Neural Networks (CNNs) show excellent performance in detecting image manipulations when they are trained on a specific forgery method. However, on examples from unseen manipulation approaches, their performance drops significantly. To address this limitation in transferability, we introduce Forensic-Transfer (FT). We devise a learning-based forensic detector which adapts well to new domains, i.e., novel manipulation methods and can handle scenarios where only a handful of fake examples are available during training. To this end, we learn a forensic embedding based on a novel autoencoder-based architecture that can be used to distinguish between real and fake imagery. The learned embedding acts as a form of anomaly detector; namely, an image manipulated from an unseen method will be detected as fake provided it maps sufficiently far away from the cluster of real images. Comparing to prior works, FT shows significant improvements in transferability, which we demonstrate in a series of experiments on cutting-edge benchmarks. For instance, on unseen examples, we achieve up to 85% in terms of accuracy, and with only a handful of seen examples, our performance already reaches around 95%.

• The paper presents a weakly-supervised autoencoder architecture that learns a discriminative latent space for robust forgery detection across diverse manipulation domains.
• It achieves impressive zero-shot and few-shot performance, reaching 85% to 95% accuracy with minimal labeled data.
• The study lays the groundwork for scalable forensic tools by improving generalization and transferability in detecting unseen image forgeries.

Analysis of Weakly-supervised Domain Adaptation for Forgery Detection

The paper introduces an innovative approach, denoted as \OURS, aimed at addressing the challenges associated with image forgery detection in scenarios involving diverse manipulation methods. The paper focuses on overcoming the limitations of traditional Convolutional Neural Network (CNN)-based classifiers, which often fail to generalize effectively when faced with unfamiliar types of image manipulations. This issue arises due to such networks' tendency to overfit on specific artifact patterns present only in the training data, leading to a significant drop in performance on novel manipulation techniques.

Methodology Overview

\OURS employs a weakly-supervised domain adaptation method, leveraging a novel autoencoder-based architecture to enhance the adaptability of forensic detectors to new manipulation domains with minimal labeled examples. The core idea is to learn a discriminative feature representation in a latent space, ensuring that distinct components of the hidden vector are activated for pristine versus forged classes. This anomaly detection mechanism allows the model to identify unseen manipulations by detecting differences in feature cluster distributions within the latent space.

Architecture and Key Features

The proposed architecture features:

• Autoencoder Structure: Employing an encoder-decoder setup, the architecture maintains a latent representation of images that is both reconstruction-aware and classification-capable. This ensures no crucial information is lost, thereby preserving generalization ability.
• Disentangled Feature Space: The latent space is divided into subspaces that activate separately for real and fake images. Training enforces the network to isolate class-discriminative features, improving its transferability to new domains.
• Reconstruction and Activation Loss: The network is trained using a composite loss function that includes reconstruction loss (to preserve image details) and activation loss (to minimize intra-class variance and maximize inter-class separation).

Experimental Results

The authors conduct extensive experiments on various image manipulation datasets, categorizing sources into domains such as GAN-generated synthetic faces, inpainting-based alterations, and face-swapping methods. The paper highlights significant improvements in zero-shot and few-shot scenarios, with \OURS often achieving accuracies between 85% and 95% with very few adaptation samples. Specifically, the paper achieves up to 85% accuracy for unseen examples and rapid performance escalation to 100% in some cases with limited target domain data.

Notably, the method demonstrates:

• Zero-Shot Transferability: Without any retraining, \OURS outperforms existing baselines significantly, maintaining high accuracy on target domains unseen during training.
• Few-Shot Adaptation: With just a handful of labeled examples from a new domain, \OURS quickly adapts, often nearing perfect accuracy, significantly faster than competing methods.

Implications and Future Research

The implications of this research are twofold. Theoretically, it advances our understanding of feature-space learning for generality and transferability in machine learning. Practically, it offers a scalable solution for real-world scenarios where access to extensive labeled data is impracticable, thus enabling more robust and versatile digital forensic tools. This work sets a foundation for future exploration in efficient domain adaptation techniques across broader manipulation contexts, including audio or video deepfakes and beyond.

Potential future developments in AI could focus on expanding these methods to multi-modal forgery detection, integrating additional contextual cues, and further refining anomaly detection capabilities. Moreover, expanding the applicability of \OURS to a more extensive array of artificial content generation methods could further enhance the adaptability and robustness of digital content authentication systems.