Exploring the Pitfalls of Black Boxes in Media Forensics: A Case Study in Source Camera Identification
The increasing reliance on deep models as black-box solutions raises critical concerns, particularly in media forensics, where explainability and robustness are essential. These models often fail to address biases in experimental design, potentially yielding misleading conclusions. To combat these r...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10975061/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The increasing reliance on deep models as black-box solutions raises critical concerns, particularly in media forensics, where explainability and robustness are essential. These models often fail to address biases in experimental design, potentially yielding misleading conclusions. To combat these risks, we advocate for rigorous hypothesis-driven methodologies that define and test competing explanatory hypotheses to reduce biases and improve reliability. We present a case study on device identification, focusing on a method proposed by Manisha et al., which claims exceptional performance using a hybrid ResNet101-SVM classifier. Our investigation reveals that this performance is likely attributable to dataset biases rather than true device-specific fingerprints. To systematically decouple content- and device-specific features, we introduce a novel “Sybil” approach, which partitions datasets based on image content. Our Sybil experiments strongly suggest that the classifier exploits content-specific biases, rather than intrinsic device fingerprints, to achieve high accuracy. We demonstrate that even datasets like FloreView, which are carefully crafted to limit biases due to the acquisition, are not immune to these biases. This analysis highlights the dangers of black-box methodologies and highlights the necessity of hypothesis-driven experimental designs in media forensics. |
|---|---|
| ISSN: | 2169-3536 |