# VADER: Video Alignment Differencing and Retrieval

arXiv: https://arxiv.org/abs/2303.13193 · HTML: https://mlsanity.com/paper/2303.13193/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 62 references that are in the map.
- First posted on arXiv 23 Mar 2023.

## Builds on (most cited first, 30 of 33)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset](https://mlsanity.com/paper/1705.07750/) (2017)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)
- [A Closer Look at Spatiotemporal Convolutions for Action Recognition](https://mlsanity.com/paper/1711.11248/) (2017)
- [FaceForensics++: Learning to Detect Manipulated Facial Images](https://mlsanity.com/paper/1901.08971/) (2019)
- [CNN-generated images are surprisingly easy to spot... for now](https://mlsanity.com/paper/1912.11035/) (2019)
- [Leveraging Frequency Analysis for Deep Fake Image Recognition](https://mlsanity.com/paper/2003.08685/) (2020)
- [Multi-attentional Deepfake Detection](https://mlsanity.com/paper/2103.02406/) (2021)
- [WildDeepfake: A Challenging Real-World Dataset for Deepfake Detection](https://mlsanity.com/paper/2101.01456/) (2021)
- [COTR: Correspondence Transformer for Matching Across Images](https://mlsanity.com/paper/2103.14167/) (2021)
- [Particular object retrieval with integral max-pooling of CNN activations](https://mlsanity.com/paper/1511.05879/) (2015)
- [MVSS-Net: Multi-View Multi-Scale Supervised Networks for Image Manipulation Detection](https://mlsanity.com/paper/2112.08935/) (2021)
- [Responsible Disclosure of Generative Models Using Scalable Fingerprinting](https://mlsanity.com/paper/2012.08726/) (2020)
- [DeepFake Detection by Analyzing Convolutional Traces](https://mlsanity.com/paper/2004.10448/) (2020)
- [Detecting Photoshopped Faces by Scripting Photoshop](https://mlsanity.com/paper/1906.05856/) (2019)
- [ViSiL: Fine-grained Spatio-Temporal Video Similarity Learning](https://mlsanity.com/paper/1908.07410/) (2019)
- [Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers](https://mlsanity.com/paper/2108.11996/) (2021)
- [RepMix: Representation Mixing for Robust Attribution of Synthesized Images](https://mlsanity.com/paper/2207.02063/) (2022)
- [AugLy: Data Augmentations for Robustness](https://mlsanity.com/paper/2201.06494/) (2022)
- [Audio-Visual Contrastive Learning with Temporal Self-Supervision](https://mlsanity.com/paper/2302.07702/) (2023)
- [Video Re-localization](https://mlsanity.com/paper/1808.01575/) (2018)
- [Detection and Localization of Facial Expression Manipulations](https://mlsanity.com/paper/2103.08134/) (2021)
- [OSCAR-Net: Object-centric Scene Graph Attention for Image Attribution](https://mlsanity.com/paper/2108.03541/) (2021)
- [TransVCL: Attention-enhanced Video Copy Localization Network with Flexible Supervision](https://mlsanity.com/paper/2211.13090/) (2022)
- [Video Similarity and Alignment Learning on Partial Video Copy Detection](https://mlsanity.com/paper/2108.01817/) (2021)
- [Circulant temporal encoding for video retrieval and temporal alignment](https://mlsanity.com/paper/1506.02588/) (2015)
- [ARCHANGEL: Trusted Archives of Digital Public Documents](https://mlsanity.com/paper/1804.08342/) (2018)

## Built on by (most cited first, 6 of 6)

- [RLGF: Reinforcement Learning with Geometric Feedback for Autonomous Driving Video Generation](https://mlsanity.com/paper/2509.16500/) (2025)
- [Sync from the Sea: Retrieving Alignable Videos from Large-Scale Datasets](https://mlsanity.com/paper/2409.01445/) (2024)
- [Counteracting temporal attacks in Video Copy Detection](https://mlsanity.com/paper/2501.11171/) (2025)
- [Sentiment Analysis On YouTube Comments Using Machine Learning Techniques Based On Video Games Content](https://mlsanity.com/paper/2511.06708/) (2025)
- [Efficient Logic Gate Networks for Video Copy Detection](https://mlsanity.com/paper/2604.21694/) (2026)
- [MLT-Dedup: Efficient Large-Scale Online Video Deduplication via Multi-Level Representations and Spatial-Temporal Matching](https://mlsanity.com/paper/2606.12215/) (2026)

## Abstract (arXiv)

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 the query fragment within the matched video. A space-time comparator module identifies regions of manipulation between aligned content, invariant to any changes due to any residual temporal misalignments or artifacts arising from non-editorial changes of the content. Robustly matching video to a trusted source enables conclusions to be drawn on video provenance, enabling informed trust decisions on content encountered.