Fragile Watermarking for Image Certification Using Deep Steganographic Embedding
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916696304910336 |
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| author | Ghiani, Davide Chivata, Jefferson David Rodriguez Lilliu, Stefano La Cava, Simone Maurizio Micheletto, Marco Orrù, Giulia Lama, Federico Marcialis, Gian Luca |
| author_facet | Ghiani, Davide Chivata, Jefferson David Rodriguez Lilliu, Stefano La Cava, Simone Maurizio Micheletto, Marco Orrù, Giulia Lama, Federico Marcialis, Gian Luca |
| contents | Modern identity verification systems increasingly rely on facial images embedded in biometric documents such as electronic passports. To ensure global interoperability and security, these images must comply with strict standards defined by the International Civil Aviation Organization (ICAO), which specify acquisition, quality, and format requirements. However, once issued, these images may undergo unintentional degradations (e.g., compression, resizing) or malicious manipulations (e.g., morphing) and deceive facial recognition systems. In this study, we explore fragile watermarking, based on deep steganographic embedding as a proactive mechanism to certify the authenticity of ICAO-compliant facial images. By embedding a hidden image within the official photo at the time of issuance, we establish an integrity marker that becomes sensitive to any post-issuance modification. We assess how a range of image manipulations affects the recovered hidden image and show that degradation artifacts can serve as robust forensic cues. Furthermore, we propose a classification framework that analyzes the revealed content to detect and categorize the type of manipulation applied. Our experiments demonstrate high detection accuracy, including cross-method scenarios with multiple deep steganography-based models. These findings support the viability of fragile watermarking via steganographic embedding as a valuable tool for biometric document integrity verification. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_13759 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fragile Watermarking for Image Certification Using Deep Steganographic Embedding Ghiani, Davide Chivata, Jefferson David Rodriguez Lilliu, Stefano La Cava, Simone Maurizio Micheletto, Marco Orrù, Giulia Lama, Federico Marcialis, Gian Luca Computer Vision and Pattern Recognition Machine Learning Modern identity verification systems increasingly rely on facial images embedded in biometric documents such as electronic passports. To ensure global interoperability and security, these images must comply with strict standards defined by the International Civil Aviation Organization (ICAO), which specify acquisition, quality, and format requirements. However, once issued, these images may undergo unintentional degradations (e.g., compression, resizing) or malicious manipulations (e.g., morphing) and deceive facial recognition systems. In this study, we explore fragile watermarking, based on deep steganographic embedding as a proactive mechanism to certify the authenticity of ICAO-compliant facial images. By embedding a hidden image within the official photo at the time of issuance, we establish an integrity marker that becomes sensitive to any post-issuance modification. We assess how a range of image manipulations affects the recovered hidden image and show that degradation artifacts can serve as robust forensic cues. Furthermore, we propose a classification framework that analyzes the revealed content to detect and categorize the type of manipulation applied. Our experiments demonstrate high detection accuracy, including cross-method scenarios with multiple deep steganography-based models. These findings support the viability of fragile watermarking via steganographic embedding as a valuable tool for biometric document integrity verification. |
| title | Fragile Watermarking for Image Certification Using Deep Steganographic Embedding |
| topic | Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2504.13759 |