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arxiv: 2606.02402 · v1 · pith:EJVFARR6new · submitted 2026-06-01 · 💻 cs.CV

Explainable Forensics of Manipulated Segments in Untrimmed Long Videos

Yue Feng , Jingjing Li , Qijia Lu , Wei Ji , Jingrou Zhang , Fei Shen , Xiao Li , Yizhen Jia
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Qiang Chen Limin Wang Wentong Li Jie Qin
This is my paper

Pith reviewed 2026-06-28 14:46 UTC · model grok-4.3

classification 💻 cs.CV
keywords video forensicsAI-generated content detectiontemporal localizationexplainable forensicslong-form video analysisdeepfake detectionmultimodal large language models
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The pith

The paper formulates a new task for localizing and explaining AI-manipulated segments in long untrimmed videos and introduces a benchmark and baseline method to address it.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

Current forensic tools for detecting AI-generated video content work only on short clips and miss cases where fake segments are scattered sparsely in long authentic videos. The authors define the Temporal AI-Generated Segment Localization and Explanation task to require detection, localization, and interpretable explanation of those segments. They release the TASLE benchmark with 12,472 videos and rich annotations, then test a baseline called MSLoc that first proposes candidate boundaries and then refines them with a multimodal large language model. A sympathetic reader would care because this shifts video forensics toward realistic long-form scenarios where misinformation often hides in plain sight within mostly real footage. If the approach succeeds, it would make segment-level analysis practical for extended videos rather than isolated clips.

Core claim

Existing video forensic methods fail on long videos because they operate on short independent clips, while realistic AI manipulations appear as sparse segments within authentic footage. This paper formulates the Temporal AI-Generated Segment Localization and Explanation task, introduces the TASLE benchmark of 12,472 untrimmed videos annotated with temporal boundaries, authenticity labels and segment-level rationales, and proposes the MSLoc baseline that combines boundary-sensitive proposal generation for efficient scanning with MLLM-based refinement for precise localization and interpretable reasoning. Experiments validate the baseline's effectiveness.

What carries the argument

The MSLoc baseline, a coarse-to-fine forensic method that first uses boundary-sensitive proposal generation for efficient long-video scanning and then applies an MLLM-based refinement module for precise boundary localization and interpretable reasoning.

If this is right

  • Segment-level explainable forensics becomes feasible for untrimmed long videos with sparse manipulations.
  • The TASLE benchmark enables standardized evaluation across diverse manipulation patterns and rich annotation signals.
  • Coarse-to-fine processing allows efficient scanning of long videos without full fine-grained analysis from the outset.
  • MLLM refinement supplies segment-level rationales that support interpretable analysis beyond binary authenticity labels.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The task formulation could support extension to streaming or live video where manipulations must be flagged in real time.
  • Emphasis on sparse segments may reduce false positives relative to methods that classify entire videos.
  • Segment-level rationales in the benchmark could aid development of detection systems that output human-readable justifications.
  • The approach points toward future benchmarks that prioritize realistic video durations over curated short clips.

Load-bearing premise

That existing short-clip forensic methods necessarily fail on realistic long videos containing only sparse manipulated segments.

What would settle it

An experiment in which an unmodified short-clip forensic method achieves comparable or higher accuracy on the TASLE benchmark than the proposed MSLoc baseline.

Figures

Figures reproduced from arXiv: 2606.02402 by Fei Shen, Jie Qin, Jingjing Li, Jingrou Zhang, Limin Wang, Qiang Chen, Qijia Lu, Wei Ji, Wentong Li, Xiao Li, Yizhen Jia, Yue Feng.

Figure 1
Figure 1. Figure 1: Illustration of the traditional AI-generated video de￾tection paradigm and the proposed long-video AI-generated segment localization and explanation task. (a) Traditional meth￾ods operate on short video clips and perform binary real–fake classification, without modeling mixed real–fake contexts in long videos. (b) In contrast, our task considers long-form videos with sparsely embedded AI-generated segments… view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the manipulation patterns and annotation granularity in TASLE. We employ three advanced generative paradigms: FLF2V and TI2V for segment-level generation, and MV2V for object-level manipulation. Green borders indicate real reference frames or videos, while red borders highlight the AI-generated segments or masks. The right panel exemplifies the annotated rationales, comprising boundary-level ra… view at source ↗
Figure 3
Figure 3. Figure 3: Statistics of our TASLE dataset in terms of (a) video duration, (b) AIGC segment duration, (c) AIGC content ratio, (d) AIGC tampering position, as well as anomaly class (d) and common phrases (d) in rationales [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Distribution of source data and AIGC tools used in the proposed TASLE Dataset. More details are in Tables 8 and 9. tics. To avoid overfitting to the original segment distribution of the source datasets, we perform random segment selec￾tion and temporal clipping. Based on the resulting textual descriptions and reference frames, we produce AI-generated video segments with various FLF2V and TI2V generators, e… view at source ↗
Figure 5
Figure 5. Figure 5: Overall architecture of the proposed MSLoc. tion, as it supports end-to-end training without task-specific designs, making it a suitable and reproducible backbone for benchmarking. While DeMamba is originally designed for short video detection, we adapt it to long-video inputs us￾ing a sliding-window strategy, enabling scalable processing without sacrificing temporal coverage. Unlike DeMamba and most exist… view at source ↗
Figure 6
Figure 6. Figure 6: Examples from the TASLE dataset. For more samples, please visit our project website via this link. The generated AI content is seamlessly integrated back into the original long videos. All videos are standardized to a resolution of 832×480 at 15 FPS. Automatic consistency checks are then performed, including verification of resolu￾tion, frame count, and temporal alignment. Finally, human inspection by six … view at source ↗
Figure 7
Figure 7. Figure 7: Overview of our human-in-the-loop dataset processing pipeline [PITH_FULL_IMAGE:figures/full_fig_p015_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Rationale annotation pipeline for TASLE. videos. As shown, our model not only accurately localizes the manipulated temporal intervals (highlighted in red), but also generates detailed explanatory texts for each interval, clearly identifying the specific objects involved (e.g., “hand motion”) and the types of anomalies (such as “rigid move￾ment” or “inconsistent occlusion”). Moreover, our model is able to c… view at source ↗
Figure 9
Figure 9. Figure 9: Visual results of long-video AI-generated segment localization and explanation. Red segments indicate the temporal intervals detected as manipulated by the model, while green segments denote authentic content. Below each interval, the model-generated explanatory texts are shown, detailing the specific objects and types of anomalies (e.g., “hand motion”, “inconsistent occlusion”). 17 [PITH_FULL_IMAGE:figur… view at source ↗
read the original abstract

The rapid advancement of AI-driven video generation has transformed content creation, while simultaneously increasing the risk of misinformation through localized manipulations in long-form videos. Existing video forensic methods predominantly operate on short, independent clips, and thus fail to capture realistic scenarios where AI-generated content is sparsely embedded within otherwise authentic footage. To bridge this gap, we formulate the task of Temporal AI-Generated Segment Localization and Explanation, which targets authenticity detection, temporal localization, and interpretable analysis of manipulated segments in untrimmed long videos. We further introduce TASLE, a large-scale benchmark comprising 12,472 untrimmed videos with diverse manipulation patterns and rich annotation signals, including temporal boundaries, authenticity labels, and segment-level rationales. In addition, we propose MSLoc, a coarse-to-fine forensic baseline that combines a boundary-sensitive proposal generation module for efficient long-video scanning with an MLLM-based refinement module for precise boundary localization and interpretable reasoning. Experiments validate the effectiveness of the proposed baseline, highlighting the importance of segment-level explainable forensics for long-form AI-generated video analysis. Our dataset and code are publicly available at https://debby-0527.github.io/TASLE.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 0 minor

Summary. The paper formulates the new task of Temporal AI-Generated Segment Localization and Explanation targeting authenticity detection, temporal localization, and interpretable analysis of manipulated segments in untrimmed long videos. It releases the TASLE benchmark of 12,472 videos annotated with temporal boundaries, authenticity labels, and segment-level rationales. It introduces the MSLoc coarse-to-fine baseline that uses boundary-sensitive proposal generation for long-video scanning followed by MLLM-based refinement for precise localization and reasoning. The manuscript states that experiments validate the effectiveness of this baseline.

Significance. If the reported experiments hold, the work is significant as the first large-scale benchmark and baseline explicitly designed for sparse manipulations in realistic long-form videos rather than short independent clips. The public release of TASLE and code is a concrete community resource. The coarse-to-fine design with MLLM refinement supplies both efficiency and segment-level explanations, directly addressing a practical gap in forensic analysis of AI-generated content.

major comments (1)
  1. [Abstract] Abstract: the claim that 'Experiments validate the effectiveness of the proposed baseline' supplies no quantitative metrics, baselines, ablation results, or error analysis, which is load-bearing for the central empirical contribution of the paper.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the careful review and the recommendation for major revision. We address the single major comment below.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that 'Experiments validate the effectiveness of the proposed baseline' supplies no quantitative metrics, baselines, ablation results, or error analysis, which is load-bearing for the central empirical contribution of the paper.

    Authors: We agree that the abstract claim is currently unsupported by numbers. The full manuscript contains quantitative results (mAP, precision/recall for localization, explanation accuracy), baseline comparisons, ablations on the coarse-to-fine design, and error analysis in Sections 4–5. To address the concern directly, we will revise the abstract to include specific metrics and comparisons that substantiate the effectiveness claim. revision: yes

Circularity Check

0 steps flagged

No significant circularity; self-contained task+dataset contribution

full rationale

The paper formulates a new task (Temporal AI-Generated Segment Localization and Explanation), releases the TASLE benchmark with annotations, and introduces a coarse-to-fine MSLoc baseline. No equations, fitted parameters, or derivations are described in the provided text. The central claims rest on the new task definition and experimental validation of the baseline rather than any reduction to prior inputs by construction. No self-citation chains or ansatzes are invoked as load-bearing premises. This matches the default expectation of a non-circular contribution.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an applied computer-vision benchmark paper; the central claims rest on the existence and utility of the released dataset and the empirical performance of the described baseline rather than on any mathematical axioms or free parameters.

pith-pipeline@v0.9.1-grok · 5770 in / 1108 out tokens · 23354 ms · 2026-06-28T14:46:54.323585+00:00 · methodology

discussion (0)

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Reference graph

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