Zobrazeno 1 - 10
of 76
pro vyhledávání: '"Masi, Iacopo"'
Image manipulation detection and localization have received considerable attention from the research community given the blooming of Generative Models (GMs). Detection methods that follow a passive approach may overfit to specific GMs, limiting their
Externí odkaz:
http://arxiv.org/abs/2409.17941
By reinterpreting a robust discriminative classifier as Energy-based Model (EBM), we offer a new take on the dynamics of adversarial training (AT). Our analysis of the energy landscape during AT reveals that untargeted attacks generate adversarial im
Externí odkaz:
http://arxiv.org/abs/2407.06315
Autor:
Beadini, Senad, Masi, Iacopo
We offer a study that connects robust discriminative classifiers trained with adversarial training (AT) with generative modeling in the form of Energy-based Models (EBM). We do so by decomposing the loss of a discriminative classifier and showing tha
Externí odkaz:
http://arxiv.org/abs/2304.04033
Differences in forgery attributes of images generated in CNN-synthesized and image-editing domains are large, and such differences make a unified image forgery detection and localization (IFDL) challenging. To this end, we present a hierarchical fine
Externí odkaz:
http://arxiv.org/abs/2303.17111
We offer a method for one-shot mask-guided image synthesis that allows controlling manipulations of a single image by inverting a quasi-robust classifier equipped with strong regularizers. Our proposed method, entitled MAGIC, leverages structured gra
Externí odkaz:
http://arxiv.org/abs/2209.11549
In this technical report, we evaluate the adversarial robustness of a very recent method called "Geometry-aware Instance-reweighted Adversarial Training"[7]. GAIRAT reports state-of-the-art results on defenses to adversarial attacks on the CIFAR-10 d
Externí odkaz:
http://arxiv.org/abs/2103.01914
Autor:
Masi, Iacopo, Killekar, Aditya, Mascarenhas, Royston Marian, Gurudatt, Shenoy Pratik, AbdAlmageed, Wael
The current spike of hyper-realistic faces artificially generated using deepfakes calls for media forensics solutions that are tailored to video streams and work reliably with a low false alarm rate at the video level. We present a method for deepfak
Externí odkaz:
http://arxiv.org/abs/2008.03412
Face segmentation is the task of densely labeling pixels on the face according to their semantics. While current methods place an emphasis on developing sophisticated architectures, use conditional random fields for smoothness, or rather employ adver
Externí odkaz:
http://arxiv.org/abs/1911.00957
Face occlusions, covering either the majority or discriminative parts of the face, can break facial perception and produce a drastic loss of information. Biometric systems such as recent deep face recognition models are not immune to obstructions or
Externí odkaz:
http://arxiv.org/abs/1906.02858
Autor:
Sabir, Ekraam, Cheng, Jiaxin, Jaiswal, Ayush, AbdAlmageed, Wael, Masi, Iacopo, Natarajan, Prem
The spread of misinformation through synthetically generated yet realistic images and videos has become a significant problem, calling for robust manipulation detection methods. Despite the predominant effort of detecting face manipulation in still i
Externí odkaz:
http://arxiv.org/abs/1905.00582