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Found 17 papers, 3 papers with code
Exploring Saliency Bias in Manipulation Detection
The social media-fuelled explosion of fake news and misinformation supported by tampered images has led to growth in the development of models and datasets for image manipulation detection.
The Age of Synthetic Realities: Challenges and Opportunities
Synthetic realities are digital creations or augmentations that are contextually generated through the use of Artificial Intelligence (AI) methods, leveraging extensive amounts of data to construct new narratives or realities, regardless of the intent to deceive.
On the Effectiveness of Image Manipulation Detection in the Age of Social Media
To this end, we introduce an anomaly enhancement loss that, when used with a residual architecture, improves the performance of different detection algorithms with a minimal introduction of false positives on the non-manipulated data.
Human Saliency-Driven Patch-based Matching for Interpretable Post-mortem Iris Recognition
Forensic iris recognition, as opposed to live iris recognition, is an emerging research area that leverages the discriminative power of iris biometrics to aid human examiners in their efforts to identify deceased persons.
Motif Mining: Finding and Summarizing Remixed Image Content
On the internet, images are no longer static; they have become dynamic content.
Forensic Analysis of Synthetically Generated Western Blot Images
As a matter of fact, the generation of synthetic content is not restricted to multimedia data like videos, photographs or audio sequences, but covers a significantly vast area that can include biological images as well, such as western blot and microscopic images.
Automatic Discovery of Political Meme Genres with Diverse Appearances
This pipeline can ingest meme images from a social network, apply computer vision-based techniques to extract local features and index new images into a database, and then organize the memes into related genres.
Learning Transformation-Aware Embeddings for Image Forensics
To establish the efficacy of the proposed approach, comparisons with state-of-the-art handcrafted and deep learning-based descriptors, and image matching approaches are made.
Dynamic Spatial Verification for Large-Scale Object-Level Image Retrieval
Images from social media can reflect diverse viewpoints, heated arguments, and expressions of creativity, adding new complexity to retrieval tasks.
Performance of Humans in Iris Recognition: The Impact of Iris Condition and Annotation-driven Verification
Results suggest that: (a) people improve their identity verification accuracy when asked to annotate matching and non-matching regions between the pair of images, (b) images depicting the same eye with large difference in pupil dilation were the most challenging to subjects, but benefited well from the annotation-driven classification, (c) humans performed better than iris recognition algorithms when verifying genuine pairs of post-mortem and disease-affected eyes (i. e., samples showing deformations that go beyond the distortions of a healthy iris due to pupil dilation), and (d) annotation does not improve accuracy of analyzing images from identical twins, which remain confusing for people.
Domain-Specific Human-Inspired Binarized Statistical Image Features for Iris Recognition
One important point is that all applications of BSIF in iris recognition have used the original BSIF filters, which were trained on image patches extracted from natural images.
Beyond Pixels: Image Provenance Analysis Leveraging Metadata
Creative works, whether paintings or memes, follow unique journeys that result in their final form.
Getting the subtext without the text: Scalable multimodal sentiment classification from visual and acoustic modalities
We discard traditional transcription features in order to minimize human intervention and to maximize the deployability of our model on at-scale real-world data.
Image Provenance Analysis at Scale
Given a large corpus of images and a query image, an interesting further step is to retrieve the set of original images whose content is present in the query image, as well as the detailed sequences of transformations that yield the query image given the original images.
Provenance Filtering for Multimedia Phylogeny
Departing from traditional digital forensics modeling, which seeks to analyze single objects in isolation, multimedia phylogeny analyzes the evolutionary processes that influence digital objects and collections over time.
U-Phylogeny: Undirected Provenance Graph Construction in the Wild
Deriving relationships between images and tracing back their history of modifications are at the core of Multimedia Phylogeny solutions, which aim to combat misinformation through doctored visual media.
Spotting the Difference: Context Retrieval and Analysis for Improved Forgery Detection and Localization
As image tampering becomes ever more sophisticated and commonplace, the need for image forensics algorithms that can accurately and quickly detect forgeries grows.
