Add WIFS2026.md for enhanced image forensics paper

_complementary/WIFS2026.md

@@ -0,0 +1,16 @@
+---
+title: Enhanced image forensics through partial C2PA-based provenance information
+paper: WIFS2026
+
+people:
+    - RiccardoZiglio
+    - EleonoraMarchesini
+    - AndreaMontibeller
+    - GiuliaBoato
+    - SilvioRanise
+    - CeciliaPasquini
+
+peopleOrder: surname
+---
+
+<!--**Dataset and Replication Package** are available [here](https://github.com/stfbk/FOWS). -->

_data/destinations.yml

@@ -1270,4 +1270,14 @@
   location: Calgary, Canada
   startDate: "2026-07-20"
   endDate: "2026-07-25"
+
+- id: WIFS2026
+  name: IEEE Workshop on Information Forensics and Security 2026
+  acronym: WIFS 2026
+  url: https://wifs2026.utt.fr/
+  type: workshop
+  location: Sendai, Japan
+  startDate: "2026-12-07"
+  endDate: "2026-12-11"
+
 # PLEASE KEEP ALPHABETICAL ORDER BY ID (IN CASE OF CONFERENCES, WITHIN YEARS)

_data/people.yml

@@ -146,6 +146,10 @@
   name: Andrea
   surname: Flamini
 
+- id: AndreaMontibeller
+  name: Andrea
+  surname: Montibeller
+
 - id: AndreaMoro
   name: Andrea
   surname: Moro
@@ -557,6 +561,10 @@
   surname: Abbiati
   email: giovanni.abbiati@unimi.it
 
+- id: GiuliaBoato
+  name: Giulia
+  surname: Boato
+
 - id: GiulioBenedetti
   name: Giulio
   surname: Benedetti

_data/publications.yml

@@ -2459,4 +2459,21 @@
   destination: PETS2026
   year: 2026
   doi: 10.56553/popets-2026-0081
+
+- id: WIFS2026
+  id_iris:
+  title: "Enhanced image forensics through partial C2PA-based provenance information"
+  authors:
+    - RiccardoZiglio
+    - EleonoraMarchesini
+    - AndreaMontibeller
+    - GiuliaBoato
+    - SilvioRanise
+    - CeciliaPasquini
+  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.
+  destination: WIFS2026
+  year: 2026
+  doi:
+
 # PLEASE KEEP ALPHABETICAL ORDER BY ID WITHIN YEARS