# Content-Aware Detection of Temporal Metadata Manipulation

arXiv: https://arxiv.org/abs/2103.04736 · HTML: https://mlsanity.com/paper/2103.04736/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 41 references that are in the map.
- First posted on arXiv 8 Mar 2021.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Fine-tuning CNN Image Retrieval with No Human Annotation](https://mlsanity.com/paper/1711.02512/) (2017)
- [Large-Scale Image Retrieval with Attentive Deep Local Features](https://mlsanity.com/paper/1612.06321/) (2016)
- [YFCC100M: The New Data in Multimedia Research](https://mlsanity.com/paper/1503.01817/) (2015)
- [Learning Rich Features for Image Manipulation Detection](https://mlsanity.com/paper/1805.04953/) (2018)
- [Fighting Fake News: Image Splice Detection via Learned Self-Consistency](https://mlsanity.com/paper/1805.04096/) (2018)
- [PlaNet - Photo Geolocation with Convolutional Neural Networks](https://mlsanity.com/paper/1602.05314/) (2016)
- [A Century of Portraits: A Visual Historical Record of American High School Yearbooks](https://mlsanity.com/paper/1511.02575/) (2015)
- [Learning a Dynamic Map of Visual Appearance](https://mlsanity.com/paper/2012.14885/) (2020)
- [Learning Geo-Temporal Image Features](https://mlsanity.com/paper/1909.07499/) (2019)
- [When was that made?](https://mlsanity.com/paper/1608.03914/) (2016)

## Built on by (most cited first, 8 of 8)

- [GT-Loc: Unifying When and Where in Images Through a Joint Embedding Space](https://mlsanity.com/paper/2507.10473/) (2025)
- [Computer Vision for Multimedia Geolocation in Human Trafficking Investigation: A Systematic Literature Review](https://mlsanity.com/paper/2402.15448/) (2024)
- [TIGeR: A Unified Framework for Time, Images and Geo-location Retrieval](https://mlsanity.com/paper/2603.24749/) (2026)
- [What Time Tells Us? An Explorative Study of Time Awareness Learned from Static Images](https://mlsanity.com/paper/2503.17899/) (2025)
- [Determining Intent of Changes to Ascertain Fake Crowdsourced Image Services](https://mlsanity.com/paper/2403.12045/) (2023)
- [Temporal Image Forensics: A Review and Critical Evaluation](https://mlsanity.com/paper/2509.07591/) (2025)
- [GTPred: Benchmarking MLLMs for Interpretable Geo-localization and Time-of-capture Prediction](https://mlsanity.com/paper/2601.13207/) (2026)
- [GeoContext: One Context Ladder, Two Failure Modes in Vision-Language Geolocation: Flat Reliance on User-Provided Location Context and False Confirmation of Location Claims](https://mlsanity.com/paper/2609.05761/) (2026)

## Abstract (arXiv)

Most pictures shared online are accompanied by temporal metadata (i.e., the day and time they were taken), which makes it possible to associate an image content with real-world events. Maliciously manipulating this metadata can convey a distorted version of reality. In this work, we present the emerging problem of detecting timestamp manipulation. We propose an end-to-end approach to verify whether the purported time of capture of an outdoor image is consistent with its content and geographic location. We consider manipulations done in the hour and/or month of capture of a photograph. The central idea is the use of supervised consistency verification, in which we predict the probability that the image content, capture time, and geographical location are consistent. We also include a pair of auxiliary tasks, which can be used to explain the network decision. Our approach improves upon previous work on a large benchmark dataset, increasing the classification accuracy from 59.0% to 81.1%. We perform an ablation study that highlights the importance of various components of the method, showing what types of tampering are detectable using our approach. Finally, we demonstrate how the proposed method can be employed to estimate a possible time-of-capture in scenarios in which the timestamp is missing from the metadata.