most citedSeeds Don't Lie: An Adaptive Watermarking Framework for Computer Vision Models

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2025

PaniCar: Securing the Perception of Advanced Driving Assistance Systems Against Emergency Vehicle Lighting

Elad Feldman, Jacob Shams, Dudi Biton +7

The safety of autonomous cars has come under scrutiny in recent years, especially after 16 documented incidents involving Teslas (with autopilot engaged) crashing into parked emerg…

cs.CV2025

A Privacy Enhancing Technique to Evade Detection by Street Video Cameras Without Using Adversarial Accessories

Jacob Shams, Ben Nassi, Satoru Koda +2

In this paper, we propose a privacy-enhancing technique leveraging an inherent property of automatic pedestrian detection algorithms, namely, that the training of deep neural netwo…

cs.CV2025

Towards an End-to-End (E2E) Adversarial Learning and Application in the Physical World

Dudi Biton, Jacob Shams, Satoru Koda +3

The traditional learning process of patch-based adversarial attacks, conducted in the digital domain and then applied in the physical domain (e.g., via printed stickers), may suffe…

cs.CV2022★ 1 cited

Seeds Don't Lie: An Adaptive Watermarking Framework for Computer Vision Models

Jacob Shams, Ben Nassi, Ikuya Morikawa +3

In recent years, various watermarking methods were suggested to detect computer vision models obtained illegitimately from their owners, however they fail to demonstrate satisfacto…

cs.CR2022★ 1 cited

bAdvertisement: Attacking Advanced Driver-Assistance Systems Using Print Advertisements

Ben Nassi, Jacob Shams, Raz Ben Netanel +1

In this paper, we present bAdvertisement, a novel attack method against advanced driver-assistance systems (ADASs). bAdvertisement is performed as a supply chain attack via a compr…