From f9aba523a768f44dfb748b372223bbfa8e4afe0e Mon Sep 17 00:00:00 2001 From: RickyZi <55984119+RickyZi@users.noreply.github.com> Date: Thu, 8 Oct 2026 15:16:47 +0200 Subject: [PATCH 1/3] Add WIFS2026.md for enhanced image forensics paper --- _complementary/WIFS2026.md | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) create mode 100644 _complementary/WIFS2026.md diff --git a/_complementary/WIFS2026.md b/_complementary/WIFS2026.md new file mode 100644 index 0000000..7d04823 --- /dev/null +++ b/_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 +--- + + From f10848f8df96e64b798312eda901af5df53418cf Mon Sep 17 00:00:00 2001 From: Marco Pernpruner Date: Wed, 14 Oct 2026 09:48:51 +0200 Subject: [PATCH 2/3] Add publication, destination and missing people --- _data/destinations.yml | 10 ++++++++++ _data/people.yml | 8 ++++++++ _data/publications.yml | 17 +++++++++++++++++ 3 files changed, 35 insertions(+) diff --git a/_data/destinations.yml b/_data/destinations.yml index 94446c0..50493b3 100644 --- a/_data/destinations.yml +++ b/_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) diff --git a/_data/people.yml b/_data/people.yml index a2eeb51..22b39d2 100644 --- a/_data/people.yml +++ b/_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 diff --git a/_data/publications.yml b/_data/publications.yml index 82df20f..ab1a3a5 100644 --- a/_data/publications.yml +++ b/_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 From 7eabf4e49cca8d7add306e523a63521d0fbb93a6 Mon Sep 17 00:00:00 2001 From: Marco Pernpruner Date: Wed, 14 Oct 2026 09:51:18 +0200 Subject: [PATCH 3/3] linting --- _data/publications.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/_data/publications.yml b/_data/publications.yml index ab1a3a5..a7c7fc1 100644 --- a/_data/publications.yml +++ b/_data/publications.yml @@ -2474,6 +2474,6 @@ 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: + doi: # PLEASE KEEP ALPHABETICAL ORDER BY ID WITHIN YEARS