Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.38639

Enhanced image forensics through partial C2PA-based provenance information

Riccardo Ziglio·Eleonora Marchesini·Andrea Montibeller·Giulia Boato·Silvio Ranise·Cecilia Pasquini
View original source ↗Read source document ↗Export metadata ↓

Abstract

Cryptographically verifiable C2PA-based Content Credentials (CCs) can be attached to freshly acquired or generated digital images but also to already existing ones with no previous provenance information, for instance after processing through trusted C2PA-enabled software tools. The latter case leads to images with so-called partial CCs, which capture only the last operations applied, while prior provenance remains unknown and can be investigated through signal forensics. This paper addresses the forensic analysis of such images, highlighting the opportunity to leverage the partial CCs as additional trusted knowledge to adapt forensic detectors towards the statistical characteristics of the image under analysis and mitigate potential distribution shifts. To this end, a two-step strategy is envisioned: (i) reconstructing the operations applied by the last signer through a structural analysis of the partial CCs; (ii) exploiting the obtained reconstruction to enhance the forensic detector of the analyst through an adaptation process. An empirical analysis on partial CCs produced by commercial C2PA-enabled software tools through different editing operations is carried out, providing evidence on the feasibility of the reconstruction in practical cases. An experimental case study on the forensic task of synthetic image detection is then presented, showing that the proposed strategy can significantly enhance the performance of state-of-the-art detectors on test images that are postprocessed via C2PA-enabled software tools after being generated/acquired.

Explore related subjects

cs.CRcs.AIcs.MMeess.IV

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Riccardo Ziglio, Eleonora Marchesini, Andrea Montibeller, Giulia Boato, Silvio Ranise, Cecilia Pasquini. 2026-10-28. Enhanced image forensics through partial C2PA-based provenance information. https://arxiv.org/abs/2610.38639

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

zkCraft: Prompt-Guided LLM as a Zero-Shot Mutation Pattern Oracle for TCCT-Powered ZK Fuzzing

Zero-knowledge circuits enable privacy-preserving and scalable systems but are difficult to implement correctly due to the tight coupling between witness computation and circuit constraints. We present zkCraft, a practical framework that combines deterministic, R1CS-aware localization with proof-bearing search to detect semantic inconsistencies. zkCraft encodes candidate constraint edits into a single Row-Vortex polynomial and replaces repeated solver queries with a Violation IOP that certifies the existence of edits together with a succinct proof. Deterministic LLM-driven mutation templates bias exploration toward edge cases while preserving auditable algebraic verification. Evaluation on real Circom code shows that proof-bearing localization detects diverse under- and over-constrained faults with low false positives and reduces costly solver interaction. Our approach bridges formal verification and automated debugging, offering a scalable path for robust ZK circuit development.

cs.CR↗
arXiv2026

Origin Lens: A Privacy-First Mobile Framework for Cryptographic Image Provenance and AI Detection

The proliferation of generative AI poses challenges for information integrity assurance, requiring systems that connect model governance with end-user verification. We present Origin Lens, a privacy-first mobile framework that targets visual disinformation through a layered verification architecture. Unlike server-side detection systems, Origin Lens performs cryptographic image provenance verification and metadata-based AI detection locally on the device via a Rust/Flutter hybrid architecture. Our system integrates multiple signals - including cryptographic provenance, generative-model metadata and watermarks, and optional retrieval-augmented verification - to provide users with graded confidence indicators at the point of consumption. We discuss the framework's alignment with regulatory requirements (EU AI Act, DSA) and its role in verification infrastructure that complements platform-level mechanisms.

cs.CR↗
arXiv2026

Data-Efficient Unknown Attack Detection in IoT Networks Using Pretrained Language Models

The rapid evolution of cyberattacks continues to drive the emergence of unknown (zero-day) threats, posing significant challenges for network intrusion detection systems in Internet of Things (IoT) networks. Existing machine learning and deep learning approaches typically rely on large labeled datasets, payload inspection, or closed-set classification, limiting their effectiveness under data scarcity, encrypted traffic, and distribution shifts. To address these limitations, we propose SiamXBERT, a data-efficient, open-set Siamese meta-learning framework built on a pretrained language model for unknown-attack detection. The framework constructs a dual-modality feature representation by integrating flow-level and packet-level information, enabling richer behavioral modeling while remaining compatible with encrypted traffic. The model learns similarity patterns for known benign and attack classes using 100 labeled samples per class. It flags traffic that does not sufficiently match any known class as potentially unknown, using a threshold set without unknown-attack data. We evaluate SiamXBERT on two IoT intrusion detection datasets against five state-of-the-art detection methods. When training and testing are performed on the same dataset at this labeling budget, SiamXBERT attains the highest unknown F1-score, exceeding the strongest competing method on each dataset by 84.8% and 37.4%; the improvement is statistically significant against all five methods on one dataset and against four of the five on the other. When a model trained on one dataset is tested on another, SiamXBERT transfers effectively in one direction but degrades in the reverse, reflecting the rejection behavior learned from the source traffic rather than the choice of threshold. These results highlight the practicality of SiamXBERT for data-efficient unknown attack detection in real-world IoT environments.

cs.CR↗