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John Collomosse - IEEE Xplore Author Profile
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John Collomosse

Also published under: J. P. Collomosse, John P. Collomosse, J. Collomosse

Publications
56
Citations
1,144
Publications by Year
20022024
Co-Authors:
Show All Co-Authors (116)
Image of author John Collomosse

John Collomosse

Also published under: J. P. Collomosse, John P. Collomosse, J. Collomosse

Affiliation

Centre for Vision Speech and Signal Processing (CVSSP), University of Surrey, UK
Adobe Research, San Jose, USA

Publication Topics

Biography

John Collomosse is currently a professor with the Centre for Vision Speech and Signal Processing, University of Surrey. His research interests are in the fusion of artificial intelligence and distributed ledger technology (DLT), and he directs the DECaDE centre comprising several UKRI activities in this area.(Based on document published on 7 May 2020).
Publications
56
Citations
1,144
Publications by Year
20022024
Co-Authors:
Show All Co-Authors (116)
Author's Published Works

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SegGuard: Defending Scene Segmentation Against Adversarial Patch Attack

Thomas Gittings;Steve Schneider;John Collomosse

2024 IEEE International Conference on Image Processing (ICIP)
Year: 2024 | Conference Paper |
Adversarial Patch Attacks (APAs) induce prediction errors by inserting carefully crafted regions into images. This paper presents the first defence against APAs for deep networks that perform semantic segmentation of scenes. We show that a conditional generator can be trained to produce patches on demand targeting specific classes and achieving superior performance versus conventional pixel-optimi...Show More

SegGuard: Defending Scene Segmentation Against Adversarial Patch Attack

Thomas Gittings;Steve Schneider;John Collomosse

Year: 2024 | Conference Paper |

PARASOL: Parametric Style Control for Diffusion Image Synthesis

Gemma Canet Tarrés;Dan Ruta;Tu Bui;John Collomosse

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2024 | Conference Paper |
We propose PARASOL, a multi-modal synthesis model that enables disentangled, parametric control of the visual style of the image by jointly conditioning synthesis on both content and a fine-grained visual style embedding. We train a latent diffusion model (LDM) using specific losses for each modality and adapt the classifer-free guidance for encouraging disentangled control over independent conten...Show More

PARASOL: Parametric Style Control for Diffusion Image Synthesis

Gemma Canet Tarrés;Dan Ruta;Tu Bui;John Collomosse

Year: 2024 | Conference Paper |

ProMark: Proactive Diffusion Watermarking for Causal Attribution

Vishal Asnani;John Collomosse;Tu Bui;Xiaoming Liu;Shruti Agarwal

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2024 | Conference Paper |
Cited by: Papers (1)
Generative AI (GenAI) is transforming creative work-flows through the capability to synthesize and manipulate images via high-level prompts. Yet creatives are not well supported to receive recognition or reward for the use of their content in GenAI training. To this end, we propose ProMark, a causal attribution technique to attribute a synthetically generated image to its training data concepts li...Show More

ProMark: Proactive Diffusion Watermarking for Causal Attribution

Vishal Asnani;John Collomosse;Tu Bui;Xiaoming Liu;Shruti Agarwal

Year: 2024 | Conference Paper |

To Authenticity, and Beyond! Building Safe and Fair Generative AI Upon the Three Pillars of Provenance

John Collomosse;Andy Parsons

IEEE Computer Graphics and Applications
Year: 2024 | Volume: 44, Issue: 3 | Magazine Article |
Cited by: Papers (1)
Provenance facts, such as who made an image and how, can provide valuable context for users to make trust decisions about visual content. Against a backdrop of inexorable progress in generative AI for computer graphics, over two billion people will vote in public elections this year. Emerging standards and provenance enhancing tools promise to play an important role in fighting fake news and the s...Show More

To Authenticity, and Beyond! Building Safe and Fair Generative AI Upon the Three Pillars of Provenance

John Collomosse;Andy Parsons

Year: 2024 | Volume: 44, Issue: 3 | Magazine Article |

VADER: Video Alignment Differencing and Retrieval

Alexander Black;Simon Jenni;Tu Bui;Md. Mehrab Tanjim;Stefano Petrangeli;Ritwik Sinha;Viswanathan Swaminathan;John Collomosse

2023 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2023 | Conference Paper |
Cited by: Papers (2)
We propose VADER, a spatio- temporal matching, alignment, and change summarization method to help fight misinformation spread via manipulated videos. VADER matches and coarsely aligns partial video fragments to candidate videos using a robust visual descriptor and scalable search over adaptively chunked video content. A transformer- based alignment module then refines the temporal localization of ...Show More

VADER: Video Alignment Differencing and Retrieval

Alexander Black;Simon Jenni;Tu Bui;Md. Mehrab Tanjim;Stefano Petrangeli;Ritwik Sinha;Viswanathan Swaminathan;John Collomosse

Year: 2023 | Conference Paper |

SceneComposer: Any-Level Semantic Image Synthesis

Yu Zeng;Zhe Lin;Jianming Zhang;Qing Liu;John Collomosse;Jason Kuen;Vishal M. Patel

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2023 | Conference Paper |
Cited by: Papers (18)
We propose a new framework for conditional image synthesis from semantic layouts of any precision levels, ranging from pure text to a 2D semantic canvas with precise shapes. More specifically, the input layout consists of one or more semantic regions with free-form text descriptions and adjustable precision levels, which can be set based on the desired controllability. The framework naturally redu...Show More

SceneComposer: Any-Level Semantic Image Synthesis

Yu Zeng;Zhe Lin;Jianming Zhang;Qing Liu;John Collomosse;Jason Kuen;Vishal M. Patel

Year: 2023 | Conference Paper |

EKILA: Synthetic Media Provenance and Attribution for Generative Art

Kar Balan;Shruti Agarwal;Simon Jenni;Andy Parsons;Andrew Gilbert;John Collomosse

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2023 | Conference Paper |
Cited by: Papers (3)
We present EKILA; a decentralized framework that enables creatives to receive recognition and reward for their contributions to generative AI (GenAI). EKILA proposes a robust visual attribution technique and combines this with an emerging content provenance standard (C2PA) to address the problem of synthetic image provenance – determining the generative model and training data responsible for an A...Show More

EKILA: Synthetic Media Provenance and Attribution for Generative Art

Kar Balan;Shruti Agarwal;Simon Jenni;Andy Parsons;Andrew Gilbert;John Collomosse

Year: 2023 | Conference Paper |

RoSteALS: Robust Steganography using Autoencoder Latent Space

Tu Bui;Shruti Agarwal;Ning Yu;John Collomosse

2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2023 | Conference Paper |
Cited by: Papers (14)
Data hiding such as steganography and invisible watermarking has important applications in copyright protection, privacy-preserved communication and content provenance. Existing works often fall short in either preserving image quality, or robustness against perturbations or are too complex to train. We propose RoSteALS, a practical steganography technique leveraging frozen pretrained autoencoders...Show More

RoSteALS: Robust Steganography using Autoencoder Latent Space

Tu Bui;Shruti Agarwal;Ning Yu;John Collomosse

Year: 2023 | Conference Paper |

Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis

Trisha Mittal;Ritwik Sinha;Viswanathan Swaminathan;John Collomosse;Dinesh Manocha

2023 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
Year: 2023 | Conference Paper |
Cited by: Papers (5)
As tools for content editing mature, and artificial intelligence (AI) based algorithms for synthesizing media grow, the presence of manipulated content across online media is increasing. This phenomenon causes the spread of misinformation, creating a greater need to distinguish between “real” and “manipulated” content. To this end, we present Videosham, a dataset consisting of 826 videos (413 real...Show More

Video Manipulations Beyond Faces: A Dataset with Human-Machine Analysis

Trisha Mittal;Ritwik Sinha;Viswanathan Swaminathan;John Collomosse;Dinesh Manocha

Year: 2023 | Conference Paper |

ARIA: Adversarially Robust Image Attribution for Content Provenance

Maksym Andriushchenko;Xiaoyang Rebecca Li;Geoffrey Oxholm;Thomas Gittings;Tu Bui;Nicolas Flammarion;John Collomosse

2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2022 | Conference Paper |
Image attribution – matching an image back to a trusted source – is an emerging tool in the fight against online misinformation. Deep visual fingerprinting models have recently been explored for this purpose. However, they are not robust to tiny input perturbations known as adversarial examples. First we illustrate how to generate valid adversarial images that can easily cause incorrect image attr...Show More

ARIA: Adversarially Robust Image Attribution for Content Provenance

Maksym Andriushchenko;Xiaoyang Rebecca Li;Geoffrey Oxholm;Thomas Gittings;Tu Bui;Nicolas Flammarion;John Collomosse

Year: 2022 | Conference Paper |

TAPESTRY: A De-Centralized Service for Trusted Interaction Online

Yifan Yang;Daniel Cooper;John Collomosse;Constantin Cătălin Drăgan;Mark Manulis;Jamie Steane;Arthi Manohar;Jo Briggs;Helen Jones;Wendy Moncur

IEEE Transactions on Services Computing
Year: 2022 | Volume: 15, Issue: 3 | Journal Article |
Cited by: Papers (4)
We present a novel de-centralised service for proving the provenance of online digital identity, exposed as an assistive tool to help non-expert users make better decisions about whom to trust online. Our service harnesses the digital personhood (DP); the longitudinal and multi-modal signals created through users’ lifelong digital interactions, as a basis for evidencing the provenance of identity....Show More

TAPESTRY: A De-Centralized Service for Trusted Interaction Online

Yifan Yang;Daniel Cooper;John Collomosse;Constantin Cătălin Drăgan;Mark Manulis;Jamie Steane;Arthi Manohar;Jo Briggs;Helen Jones;Wendy Moncur

Year: 2022 | Volume: 15, Issue: 3 | Journal Article |

OSCAR-Net: Object-centric Scene Graph Attention for Image Attribution

Eric Nguyen;Tu Bui;Viswanathan Swaminathan;John Collomosse

2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (11)
Images tell powerful stories but cannot always be trusted. Matching images back to trusted sources (attribution) enables users to make a more informed judgment of the images they encounter online. We propose a robust image hashing algorithm to perform such matching. Our hash is sensitive to manipulation of subtle, salient visual details that can substantially change the story told by an image. Yet...Show More

OSCAR-Net: Object-centric Scene Graph Attention for Image Attribution

Eric Nguyen;Tu Bui;Viswanathan Swaminathan;John Collomosse

Year: 2021 | Conference Paper |

ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similarity

Dan Ruta;Saeid Motiian;Baldo Faieta;Zhe Lin;Hailin Jin;Alex Filipkowski;Andrew Gilbert;John Collomosse

2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (15)
We present ALADIN (All Layer AdaIN); a novel architecture for searching images based on the similarity of their artistic style. Representation learning is critical to visual search, where distance in the learned search embedding reflects image similarity. Learning an embedding that discriminates fine-grained variations in style is hard, due to the difficulty of defining and labelling style. ALADIN...Show More

ALADIN: All Layer Adaptive Instance Normalization for Fine-grained Style Similarity

Dan Ruta;Saeid Motiian;Baldo Faieta;Zhe Lin;Hailin Jin;Alex Filipkowski;Andrew Gilbert;John Collomosse

Year: 2021 | Conference Paper |

Scene Designer: a Unified Model for Scene Search and Synthesis from Sketch

Leo Sampaio Ferraz Ribeiro;Tu Bui;John Collomosse;Moacir Ponti

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)
Year: 2021 | Conference Paper |
Cited by: Papers (2)
Scene Designer is a novel method for searching and generating images using free-hand sketches of scene compositions; i.e. drawings that describe both the appearance and relative positions of objects. Our core contribution is a single unified model to learn both a cross-modal search embedding for matching sketched compositions to images, and an object embedding for layout synthesis. We show that a ...Show More

Scene Designer: a Unified Model for Scene Search and Synthesis from Sketch

Leo Sampaio Ferraz Ribeiro;Tu Bui;John Collomosse;Moacir Ponti

Year: 2021 | Conference Paper |

Magic Layouts: Structural Prior for Component Detection in User Interface Designs

Dipu Manandhar;Hailin Jin;John Collomosse

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2021 | Conference Paper |
Cited by: Papers (1)
We present Magic Layouts; a method for parsing screen-shots or hand-drawn sketches of user interface (UI) layouts. Our core contribution is to extend existing detectors to exploit a learned structural prior for UI designs, enabling robust detection of UI components; buttons, text boxes and similar. Specifically we learn a prior over mobile UI layouts, encoding common spatial co-occurrence relation...Show More

Magic Layouts: Structural Prior for Component Detection in User Interface Designs

Dipu Manandhar;Hailin Jin;John Collomosse

Year: 2021 | Conference Paper |

Deep Image Comparator: Learning to Visualize Editorial Change

Alexander Black;Tu Bui;Hailin Jin;Vishy Swaminathan;John Collomosse

2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2021 | Conference Paper |
Cited by: Papers (6)
We present a novel architecture for comparing a pair of images to identify image regions that have been subjected to editorial manipulation. We first describe a robust near-duplicate search, for matching a potentially manipulated image circulating online to an image within a trusted database of originals. We then describe a novel architecture for comparing that image pair, to localize regions that...Show More

Deep Image Comparator: Learning to Visualize Editorial Change

Alexander Black;Tu Bui;Hailin Jin;Vishy Swaminathan;John Collomosse

Year: 2021 | Conference Paper |

Sketchformer: Transformer-Based Representation for Sketched Structure

Leo Sampaio Ferraz Ribeiro;Tu Bui;John Collomosse;Moacir Ponti

2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020 | Conference Paper |
Cited by: Papers (53)
Sketchformer is a novel transformer-based representation for encoding free-hand sketches input in a vector form, i.e. as a sequence of strokes. Sketchformer effectively addresses multiple tasks: sketch classification, sketch based image retrieval (SBIR), and the reconstruction and interpolation of sketches. We report several variants exploring continuous and tokenized input representations, and co...Show More

Sketchformer: Transformer-Based Representation for Sketched Structure

Leo Sampaio Ferraz Ribeiro;Tu Bui;John Collomosse;Moacir Ponti

Year: 2020 | Conference Paper |

Tamper-Proofing Video With Hierarchical Attention Autoencoder Hashing on Blockchain

Tu Bui;Daniel Cooper;John Collomosse;Mark Bell;Alex Green;John Sheridan;Jez Higgins;Arindra Das;Jared Robert Keller;Olivier Thereaux

IEEE Transactions on Multimedia
Year: 2020 | Volume: 22, Issue: 11 | Journal Article |
Cited by: Papers (17)
We present ARCHANGEL; a novel distributed ledger based system for assuring the long-term integrity of digital video archives. First, we introduce a novel deep network architecture using a hierarchical attention autoencoder (HAAE) to compute temporal content hashes (TCHs) from minutes or hour-long audio-visual streams. Our TCHs are sensitive to accidental or malicious content modification (tamperin...Show More

Tamper-Proofing Video With Hierarchical Attention Autoencoder Hashing on Blockchain

Tu Bui;Daniel Cooper;John Collomosse;Mark Bell;Alex Green;John Sheridan;Jez Higgins;Arindra Das;Jared Robert Keller;Olivier Thereaux

Year: 2020 | Volume: 22, Issue: 11 | Journal Article |

Inpainting of Wide-Baseline Multiple Viewpoint Video

Andrew Gilbert;Matthew Trumble;Adrian Hilton;John Collomosse

IEEE Transactions on Visualization and Computer Graphics
Year: 2020 | Volume: 26, Issue: 7 | Journal Article |
Cited by: Papers (4)
We describe a non-parametric algorithm for multiple-viewpoint video inpainting. Uniquely, our algorithm addresses the domain of wide baseline multiple-viewpoint video (MVV) with no temporal look-ahead in near real time speed. A Dictionary of Patches (DoP) is built using multi-resolution texture patches reprojected from geometric proxies available in the alternate views. We dynamically update the D...Show More

Inpainting of Wide-Baseline Multiple Viewpoint Video

Andrew Gilbert;Matthew Trumble;Adrian Hilton;John Collomosse

Year: 2020 | Volume: 26, Issue: 7 | Journal Article |

ARCHANGEL: Tamper-Proofing Video Archives Using Temporal Content Hashes on the Blockchain

Tu Bui;Daniel Cooper;John Collomosse;Mark Bell;Alex Green;John Sheridan;Jez Higgins;Arindra Das;Jared Keller;Olivier Thereaux;Alan Brown

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Year: 2019 | Conference Paper |
Cited by: Papers (16)
We present ARCHANGEL; a novel distributed ledger based system for assuring the long-term integrity of digital video archives. First, we describe a novel deep network architecture for computing compact temporal content hashes (TCHs) from audio-visual streams with durations of minutes or hours. Our TCHs are sensitive to accidental or malicious content modification (tampering) but invariant to the co...Show More

ARCHANGEL: Tamper-Proofing Video Archives Using Temporal Content Hashes on the Blockchain

Tu Bui;Daniel Cooper;John Collomosse;Mark Bell;Alex Green;John Sheridan;Jez Higgins;Arindra Das;Jared Keller;Olivier Thereaux;Alan Brown

Year: 2019 | Conference Paper |

An Internal Learning Approach to Video Inpainting

Haotian Zhang;Long Mai;Hailin Jin;Zhaowen Wang;Ning Xu;John Collomosse

2019 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2019 | Conference Paper |
Cited by: Papers (48)
We propose a novel video inpainting algorithm that simultaneously hallucinates missing appearance and motion (optical flow) information, building upon the recent 'Deep Image Prior' (DIP) that exploits convolutional network architectures to enforce plausible texture in static images. In extending DIP to video we make two important contributions. First, we show that coherent video inpainting is poss...Show More

An Internal Learning Approach to Video Inpainting

Haotian Zhang;Long Mai;Hailin Jin;Zhaowen Wang;Ning Xu;John Collomosse

Year: 2019 | Conference Paper |

LiveSketch: Query Perturbations for Guided Sketch-Based Visual Search

John Collomosse;Tu Bui;Hailin Jin

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2019 | Conference Paper |
Cited by: Papers (38)
LiveSketch is a novel algorithm for searching large image collections using hand-sketched queries. LiveSketch tackles the inherent ambiguity of sketch search by creating visual suggestions that augment the query as it is drawn, making query specification an iterative rather than one-shot process that helps disambiguate users' search intent. Our technical contributions are: a triplet convnet archit...Show More

LiveSketch: Query Perturbations for Guided Sketch-Based Visual Search

John Collomosse;Tu Bui;Hailin Jin

Year: 2019 | Conference Paper |

TAPESTRY: Visualizing Interwoven Identities for Trust Provenance

Yifan Yang;John Collomosse;Arthi Kanchana Manohar;Jo Briggs;Jamie Steane

2018 IEEE Symposium on Visualization for Cyber Security (VizSec)
Year: 2018 | Conference Paper |
Cited by: Papers (5)
In this paper we report our study involving an early prototype of TAPESTRY, a service to support people and businesses to connect safely online through the use of a Machine Learning generated visualization. Establishing the veracity of the person or business behind a pseudonomized identity, online, is a challenge for many people. In the burgeoning digital economy, finding ways to support good deci...Show More

TAPESTRY: Visualizing Interwoven Identities for Trust Provenance

Yifan Yang;John Collomosse;Arthi Kanchana Manohar;Jo Briggs;Jamie Steane

Year: 2018 | Conference Paper |

Disentangling Structure and Aesthetics for Style-Aware Image Completion

Andrew Gilbert;John Collomosse;Hailin Jin;Brian Price

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
Year: 2018 | Conference Paper |
Cited by: Papers (10)
Content-aware image completion or in-painting is a fundamental tool for the correction of defects or removal of objects in images. We propose a non-parametric in-painting algorithm that enforces both structural and aesthetic (style) consistency within the resulting image. Our contributions are two-fold: (1) we explicitly disentangle image structure and style during patch search and selection to en...Show More

Disentangling Structure and Aesthetics for Style-Aware Image Completion

Andrew Gilbert;John Collomosse;Hailin Jin;Brian Price

Year: 2018 | Conference Paper |

Real-Time Full-Body Motion Capture from Video and IMUs

Charles Malleson;Andrew Gilbert;Matthew Trumble;John Collomosse;Adrian Hilton;Marco Volino

2017 International Conference on 3D Vision (3DV)
Year: 2017 | Conference Paper |
Cited by: Papers (70)
A real-time full-body motion capture system is presented which uses input from a sparse set of inertial measurement units (IMUs) along with images from two or more standard video cameras and requires no optical markers or specialized infra-red cameras. A real-time optimization-based framework is proposed which incorporates constraints from the IMUs, cameras and a prior pose model. The combination ...Show More

Real-Time Full-Body Motion Capture from Video and IMUs

Charles Malleson;Andrew Gilbert;Matthew Trumble;John Collomosse;Adrian Hilton;Marco Volino

Year: 2017 | Conference Paper |

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