90 citations · 192 across the 22 of their papers we have counts for
27 papers
MOTIF: Person-of-Interest Deepfake Detection Beyond 3DMM Coefficients
Giovanni Affatato, Sara Mandelli, Paolo Bestagini +1
Video deepfakes targeting a specific individual, the Person-of-Interest (POI), are the most harmful ones, and, since a public figure is abundantly recorded, a detector can be built…
CUPID: Reconstructing UV Texture Maps for Interpretable Person-of-Interest Deepfake Detection
Giovanni Affatato, Sara Mandelli, Edoardo Daniele Cannas +2
Deepfakes targeting a high-profile individual, known as Person-of-Interest (POI), are a threat to modern democracies and societies. Current POI deepfake detection methods still str…
Beyond Spectral Peaks: Interpreting the Cues Behind Synthetic Image Detection
Sara Mandelli, Diego Vila-Portela, David Vázquez-Padín +2
Over the years, the forensics community has proposed several deep learning-based detectors to mitigate the risks of generative AI. Recently, frequency-domain artifacts (particularl…
Phoneme-Level Analysis for Person-of-Interest Speech Deepfake Detection
Davide Salvi, Viola Negroni, Sara Mandelli +2
Recent advances in generative AI have made the creation of speech deepfakes widely accessible, posing serious challenges to digital trust. To counter this, various speech deepfake…
WILD: a new in-the-Wild Image Linkage Dataset for synthetic image attribution
Pietro Bongini, Sara Mandelli, Andrea Montibeller +14
Synthetic image source attribution is an open challenge, with an increasing number of image generators being released yearly. The complexity and the sheer number of available gener…
Leveraging Land Cover Priors for Isoprene Emission Super-Resolution
Christopher Ummerle, Antonio Giganti, Sara Mandelli +2
Remote sensing plays a crucial role in monitoring Earth's ecosystems, yet satellite-derived data often suffer from limited spatial resolution, restricting their applicability in at…
Adaptive Mixture of Low-Rank Experts for Robust Audio Spoofing Detection
Qixian Chen, Yuxiong Xu, Sara Mandelli +2
In audio spoofing detection, most studies rely on clean datasets, making models susceptible to real-world post-processing attacks, such as channel compression and noise. To overcom…
Is JPEG AI going to change image forensics?
Edoardo Daniele Cannas, Sara Mandelli, Nataša Popović +4
In this paper, we investigate the counter-forensic effects of the new JPEG AI standard based on neural image compression, focusing on two critical areas: deepfake image detection a…
POLIPHONE: A Dataset for Smartphone Model Identification from Audio Recordings
Davide Salvi, Daniele Ugo Leonzio, Antonio Giganti +4
When dealing with multimedia data, source attribution is a key challenge from a forensic perspective. This task aims to determine how a given content was captured, providing valuab…
Explainable Artifacts for Synthetic Western Blot Source Attribution
João Phillipe Cardenuto, Sara Mandelli, Daniel Moreira +3
Recent advancements in artificial intelligence have enabled generative models to produce synthetic scientific images that are indistinguishable from pristine ones, posing a challen…
Localization of Synthetic Manipulations in Western Blot Images
Anmol Manjunath, Viola Negroni, Sara Mandelli +2
Recent breakthroughs in deep learning and generative systems have significantly fostered the creation of synthetic media, as well as the local alteration of real content via the in…
Deepfake Media Forensics: State of the Art and Challenges Ahead
Irene Amerini, Mauro Barni, Sebastiano Battiato +21
AI-generated synthetic media, also called Deepfakes, have significantly influenced so many domains, from entertainment to cybersecurity. Generative Adversarial Networks (GANs) and…
When Synthetic Traces Hide Real Content: Analysis of Stable Diffusion Image Laundering
Sara Mandelli, Paolo Bestagini, Stefano Tubaro
In recent years, methods for producing highly realistic synthetic images have significantly advanced, allowing the creation of high-quality images from text prompts that describe t…
Hiding Local Manipulations on SAR Images: a Counter-Forensic Attack
Sara Mandelli, Edoardo Daniele Cannas, Paolo Bestagini +2
The vast accessibility of Synthetic Aperture Radar (SAR) images through online portals has propelled the research across various fields. This widespread use and easy availability h…
Super-Resolution of BVOC Emission Maps Via Domain Adaptation
Antonio Giganti, Sara Mandelli, Paolo Bestagini +2
Enhancing the resolution of Biogenic Volatile Organic Compound (BVOC) emission maps is a critical task in remote sensing. Recently, some Super-Resolution (SR) methods based on Deep…
Multi-BVOC Super-Resolution Exploiting Compounds Inter-Connection
Antonio Giganti, Sara Mandelli, Paolo Bestagini +2
Biogenic Volatile Organic Compounds (BVOCs) emitted from the terrestrial ecosystem into the Earth's atmosphere are an important component of atmospheric chemistry. Due to the scarc…
Super-Resolution of BVOC Maps by Adapting Deep Learning Methods
Antonio Giganti, Sara Mandelli, Paolo Bestagini +2
Biogenic Volatile Organic Compounds (BVOCs) play a critical role in biosphere-atmosphere interactions, being a key factor in the physical and chemical properties of the atmosphere…
Detecting GAN-generated Images by Orthogonal Training of Multiple CNNs
Sara Mandelli, Nicolò Bonettini, Paolo Bestagini +1
In the last few years, we have witnessed the rise of a series of deep learning methods to generate synthetic images that look extremely realistic. These techniques prove useful in…
Amplitude SAR Imagery Splicing Localization
Edoardo Daniele Cannas, Nicolò Bonettini, Sara Mandelli +2
Synthetic Aperture Radar (SAR) images are a valuable asset for a wide variety of tasks. In the last few years, many websites have been offering them for free in the form of easy to…
Forensic Analysis of Synthetically Generated Western Blot Images
Sara Mandelli, Davide Cozzolino, Edoardo D. Cannas +8
The widespread diffusion of synthetically generated content is a serious threat that needs urgent countermeasures. As a matter of fact, the generation of synthetic content is not r…
DIPPAS: A Deep Image Prior PRNU Anonymization Scheme
Francesco Picetti, Sara Mandelli, Paolo Bestagini +2
Source device identification is an important topic in image forensics since it allows to trace back the origin of an image. Its forensics counter-part is source device anonymizatio…
Training CNNs in Presence of JPEG Compression: Multimedia Forensics vs Computer Vision
Sara Mandelli, Nicolò Bonettini, Paolo Bestagini +1
Convolutional Neural Networks (CNNs) have proved very accurate in multiple computer vision image classification tasks that required visual inspection in the past (e.g., object reco…
A Modified Fourier-Mellin Approach for Source Device Identification on Stabilized Videos
Sara Mandelli, Fabrizio Argenti, Paolo Bestagini +3
To decide whether a digital video has been captured by a given device, multimedia forensic tools usually exploit characteristic noise traces left by the camera sensor on the acquir…
Video Face Manipulation Detection Through Ensemble of CNNs
Nicolò Bonettini, Edoardo Daniele Cannas, Sara Mandelli +3
In the last few years, several techniques for facial manipulation in videos have been successfully developed and made available to the masses (i.e., FaceSwap, deepfake, etc.). Thes…