End-to-End Multimodal Data Augmentation and Adversarial Robustness Benchmark with AugLy for Images, Text, Audio, and PyTorch
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A tutorial shows AugLy augmentations and small robustness tests for image, text, and audio in PyTorch.
MarkTechPost published a self-contained tutorial that uses Meta's AugLy library to augment images, text, and audio and to measure robustness. The walkthrough covers functional and class APIs, metadata and intensity tracking, probabilistic composition, bounding-box-aware transforms, and custom transforms. It benchmarks perceptual-hash copy detection under image distortions and evaluates a small template-based sentiment classifier against adversarial text, Unicode obfuscation, sanitization, and adversarial training. The examples rely on synthetic data and connect AugLy transforms to PyTorch datasets and DataLoaders.
- Tutorial uses AugLy for image, text, and audio augmentation.
- Covers metadata, intensity, composition, and bounding-box transforms.
- Benchmarks perceptual-hash copy detection under image distortions.
- Tests a tiny text classifier against obfuscation and adversarial training.
- Experiments use synthetic data wired into PyTorch DataLoaders.
Full article2,204 words · extracted from marktechpost.com · click to collapse
In this tutorial, we build a comprehensive multimodal augmentation and robustness workflow with AugLy for images, text, and audio. We start by addressing modern dependency compatibility issues and generating deterministic synthetic datasets so the experiments remain self-contained and reproducible. We then explore AugLy’s functional and class-based APIs, metadata, and intensity tracking, probabilistic composition, bounding-box-aware transformations, and custom transforms. We extend the workflow into practical robustness experiments by benchmarking perceptual-hash copy detection under image distortions and evaluating text classifiers against adversarial perturbations, Unicode obfuscation, sanitization, and adversarial training. We also integrate audio augmentation, build a queryable metadata warehouse, and connect AugLy transformations directly to PyTorch datasets and DataLoaders, giving us an end-to-end view of augmentation as both a data-generation mechanism and a measurable robustness tool.
import subprocess, sys, importlib
def _sh(cmd):
print(f"$ {cmd}")
subprocess.run(cmd, shell=True, check=False,
stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL)
def _need(mod):
try:
importlib.import_module(mod)
return False
except ImportError:
return True
if _need("augly"):
_sh("apt-get -qq install -y libmagic1 > /dev/null 2>&1")
_sh(f'"{sys.executable}" -m pip install -q --no-deps augly')
_sh(f'"{sys.executable}" -m pip install -q "iopath>=0.1.8" "python-magic>=0.4.22" '
f'"regex>=2021.4.4" "nlpaug==1.1.3"')
import numpy as np
from PIL import Image, ImageDraw, ImageFont, ImageFilter
for _name, _builtin in (("float", float), ("int", int), ("bool", bool)):
if not hasattr(np, _name):
setattr(np, _name, _builtin)
def _size(font, text):
left, top, right, bottom = font.getbbox(text)
return (right, bottom)
if not hasattr(ImageFont.FreeTypeFont, "getsize"):
ImageFont.FreeTypeFont.getsize = lambda self, t, *a, **k: _size(self, t)
if not hasattr(ImageFont.FreeTypeFont, "getsize_multiline"):
def _getsize_multiline(self, text, direction=None, spacing=4, features=None,
language=None, stroke_width=0):
lines = text.split("\n")
w = max((_size(self, ln)[0] for ln in lines), default=0)
h = sum(_size(self, ln)[1] for ln in lines) + spacing * (len(lines) - 1)
return (w, h)
ImageFont.FreeTypeFont.getsize_multiline = _getsize_multiline
import os, io, json, math, random, string, textwrap, unicodedata, warnings
from dataclasses import dataclass
from typing import Any, Dict, List, Optional, Tuple
import matplotlib.pyplot as plt
import pandas as pd
import augly.image as imaugs
import augly.text as textaugs
import augly.utils as augutils
from augly.image.transforms import BaseTransform as ImageBaseTransform
warnings.filterwarnings("ignore")
pd.set_option("display.width", 160)
SEED = 1234
random.seed(SEED)
np.random.seed(SEED)
print("\n" + "=" * 78)
print("AugLy ready. assets at:", augutils.ASSETS_BASE_DIR)
print("image augs :", len([f for f in dir(imaugs) if f[0].islower()]))
print("text augs :", len([f for f in dir(textaugs) if f[0].islower()]))
print("=" * 78 + "\n")
def make_image(idx: int, w: int = 320, h: int = 240) -> Tuple[Image.Image, Tuple[int, int, int, int]]:
"""Procedurally generated 'photo' + a ground-truth bbox in pascal_voc format."""
rng = random.Random(SEED + idx)
img = Image.new("RGB", (w, h), tuple(rng.randint(20, 90) for _ in range(3)))
d = ImageDraw.Draw(img)
for _ in range(70):
x0, y0 = rng.randint(0, w), rng.randint(0, h)
d.line([x0, y0, x0 + rng.randint(-60, 60), y0 + rng.randint(-60, 60)],
fill=tuple(rng.randint(60, 160) for _ in range(3)), width=rng.randint(1, 3))
ow, oh = rng.randint(70, 130), rng.randint(60, 110)
ox, oy = rng.randint(10, w - ow - 10), rng.randint(10, h - oh - 10)
box = (ox, oy, ox + ow, oy + oh)
colour = tuple(rng.randint(150, 255) for _ in range(3))
if idx % 3 == 0:
d.ellipse(box, fill=colour, outline=(255, 255, 255), width=3)
elif idx % 3 == 1:
d.rectangle(box, fill=colour, outline=(255, 255, 255), width=3)
else:
d.polygon([(ox + ow // 2, oy), (ox + ow, oy + oh), (ox, oy + oh)],
fill=colour, outline=(255, 255, 255))
return img, box
N_IMAGES = 24
IMAGES, BOXES = zip(*[make_image(i) for i in range(N_IMAGES)])
IMAGES, BOXES = list(IMAGES), list(BOXES)
DEMO_IMG, DEMO_BOX = IMAGES[0], BOXES[0]
def make_text_dataset(n_per_class: int = 260):
"""Tiny sentiment corpus built from templates -> learnable but not trivial."""
rng = random.Random(SEED)
pos_adj = ["excellent", "delightful", "superb", "charming", "brilliant",
"flawless", "wonderful", "outstanding", "impressive", "lovely"]
neg_adj = ["terrible", "awful", "dreadful", "disappointing", "clumsy",
"broken", "miserable", "useless", "painful", "sloppy"]
subj = ["the movie", "this restaurant", "the hotel room", "their support team",
"the new phone", "the sequel", "this laptop", "the delivery service"]
tail_p = ["and I would recommend it to anyone", "worth every rupee",
"I left completely satisfied", "easily the best of the year",
"it exceeded all my expectations"]
tail_n = ["and I want a refund", "a total waste of money",
"I left extremely frustrated", "easily the worst of the year",
"it failed every expectation"]
rows = []
for _ in range(n_per_class):
rows.append((f"{rng.choice(subj)} was {rng.choice(pos_adj)} {rng.choice(tail_p)}", 1))
rows.append((f"{rng.choice(subj)} was {rng.choice(neg_adj)} {rng.choice(tail_n)}", 0))
rng.shuffle(rows)
return [r[0] for r in rows], [r[1] for r in rows]
TEXTS, LABELS = make_text_dataset()
DEMO_TEXT = "The quick brown fox jumps over the lazy dog near the river bank"
def make_audio(seconds: float = 2.0, sr: int = 16000) -> Tuple[np.ndarray, int]:
"""A chirp + harmonics + a little noise = something you can actually hear change."""
t = np.linspace(0, seconds, int(sr * seconds), endpoint=False)
f = np.linspace(220, 880, t.size)
sig = 0.5 * np.sin(2 * np.pi * f * t) + 0.2 * np.sin(2 * np.pi * 2 * f * t)
sig += 0.02 * np.random.RandomState(SEED).randn(t.size)
env = np.minimum(1.0, np.minimum(t * 8, (seconds - t) * 8))
return (sig * env).astype(np.float32), sr
AUDIO, SR = make_audio()
def show_grid(pairs, cols=4, title="", figsize_scale=2.9):
"""pairs: list of (caption, PIL.Image)."""
rows = math.ceil(len(pairs) / cols)
fig, axes = plt.subplots(rows, cols, figsize=(cols * figsize_scale, rows * figsize_scale))
axes = np.atleast_1d(axes).ravel()
for ax, (cap, im) in zip(axes, pairs):
ax.imshow(im)
ax.set_title(cap, fontsize=8)
ax.axis("off")
for ax in axes[len(pairs):]:
ax.axis("off")
if title:
fig.suptitle(title, fontsize=13, y=1.0)
plt.tight_layout()
plt.show()
def as_str(out) -> str:
"""AugLy text augs return str for str input in some transforms, list in others."""
return out[0] if isinstance(out, list) else out
print("\n### §2 IMAGE AUGMENTATION + METADATA " + "#" * 38)
functional_result = imaugs.pixelization(DEMO_IMG, ratio=0.25)
class_result = imaugs.Pixelization(ratio=0.25, p=1.0)(DEMO_IMG)
print("functional == class:", np.array_equal(np.array(functional_result), np.array(class_result)))
IMAGE_ZOO = {
"blur": lambda im, m: imaugs.blur(im, radius=3.0, metadata=m),
"brightness": lambda im, m: imaugs.brightness(im, factor=1.7, metadata=m),
"color_jitter": lambda im, m: imaugs.color_jitter(im, brightness_factor=1.3,
contrast_factor=1.4,
saturation_factor=1.6, metadata=m),
"crop": lambda im, m: imaugs.crop(im, x1=.15, y1=.15, x2=.85, y2=.85, metadata=m),
"encoding_quality": lambda im, m: imaugs.encoding_quality(im, quality=8, metadata=m),
"grayscale": lambda im, m: imaugs.grayscale(im, metadata=m),
"hflip": lambda im, m: imaugs.hflip(im, metadata=m),
"meme_format": lambda im, m: imaugs.meme_format(im, text="TOP TEXT",
caption_height=90, metadata=m),
"opacity": lambda im, m: imaugs.opacity(im, level=0.45, metadata=m),
"overlay_emoji": lambda im, m: imaugs.overlay_emoji(im, opacity=0.9,
emoji_size=0.35, metadata=m),
"overlay_screenshot": lambda im, m: imaugs.overlay_onto_screenshot(im, metadata=m),
"overlay_stripes": lambda im, m: imaugs.overlay_stripes(im, line_width=0.4,
line_opacity=0.7, metadata=m),
"overlay_text": lambda im, m: imaugs.overlay_text(im, opacity=0.9, metadata=m),
"pad_square": lambda im, m: imaugs.pad_square(im, metadata=m),
"perspective": lambda im, m: imaugs.perspective_transform(im, sigma=40.0, metadata=m),
"pixelization": lambda im, m: imaugs.pixelization(im, ratio=0.15, metadata=m),
"random_noise": lambda im, m: imaugs.random_noise(im, var=0.03, metadata=m),
"rotate": lambda im, m: imaugs.rotate(im, degrees=17, metadata=m),
"saturation": lambda im, m: imaugs.saturation(im, factor=3.0, metadata=m),
"scale": lambda im, m: imaugs.scale(im, factor=0.35, metadata=m),
"sharpen": lambda im, m: imaugs.sharpen(im, factor=8.0, metadata=m),
"shuffle_pixels": lambda im, m: imaugs.shuffle_pixels(im, factor=0.15, metadata=m),
"skew": lambda im, m: imaugs.skew(im, skew_factor=0.35, metadata=m),
"vflip": lambda im, m: imaugs.vflip(im, metadata=m),
}
gallery, image_meta = [("ORIGINAL", DEMO_IMG)], []
for name, fn in IMAGE_ZOO.items():
m = []
try:
out = fn(DEMO_IMG, m)
gallery.append((f"{name}\nintensity={m[0]['intensity']:.1f}", out))
image_meta.append(m[0])
except Exception as e:
print(f" [skip] {name}: {type(e).__name__}: {e}")
show_grid(gallery, cols=5, title="§2 AugLy image augmentations (with AugLy's own intensity score)")
meta_df = pd.DataFrame(image_meta)[["name", "intensity", "src_width", "src_height",
"dst_width", "dst_height"]]
print(meta_df.sort_values("intensity", ascending=False).head(10).to_string(index=False))
We set up AugLy in a modern Colab environment while adding compatibility shims for NumPy and Pillow. We generate deterministic synthetic image, text, and audio datasets without external downloads. We also initialize reusable visualization and utility functions before exploring image augmentation and metadata.
print("\n### §3 COMPOSITION & REPRODUCIBILITY " + "#" * 39)
REUPLOAD_PIPELINE = imaugs.Compose([
imaugs.OneOf([
imaugs.OverlayOntoScreenshot(),
imaugs.MemeFormat(text="LOL", caption_height=80),
imaugs.OverlayStripes(line_width=0.3, line_opacity=0.5),
], p=0.9),
imaugs.RandomAspectRatio(min_ratio=0.7, max_ratio=1.4, p=0.5),
imaugs.RandomEmojiOverlay(p=0.7),
imaugs.RandomBrightness(min_factor=0.7, max_factor=1.4, p=0.6),
imaugs.EncodingQuality(quality=12, p=1.0),
])
def run_pipeline(img, seed=None):
"""AugLy image transforms use the global `random` module -> seed it for determinism."""
if seed is not None:
random.seed(seed)
np.random.seed(seed)
meta = []
return REUPLOAD_PIPELINE(img, metadata=meta), meta
a, meta_a = run_pipeline(DEMO_IMG, seed=7)
b, meta_b = run_pipeline(DEMO_IMG, seed=7)
c, _ = run_pipeline(DEMO_IMG, seed=99)
print("same seed -> identical output:", np.array_equal(np.array(a), np.array(b)))
print("applied chain (seed=7) :", " -> ".join(m["name"] for m in meta_a))
show_grid([("original", DEMO_IMG), ("seed=7", a), ("seed=7 again", b), ("seed=99", c)],
cols=4, title="§3 Seeded, reproducible augmentation pipelines")
print("\n### §4 BBOX-AWARE AUGMENTATION " + "#" * 45)
BBOX_OPS = [
("crop", lambda im, m, bb: imaugs.crop(im, x1=.1, y1=.1, x2=.9, y2=.9,
metadata=m, bboxes=bb, bbox_format="pascal_voc")),
("hflip", lambda im, m, bb: imaugs.hflip(im, metadata=m, bboxes=bb,
bbox_format="pascal_voc")),
("rotate 20", lambda im, m, bb: imaugs.rotate(im, degrees=20, metadata=m, bboxes=bb,
bbox_format="pascal_voc")),
("pad", lambda im, m, bb: imaugs.pad(im, w_factor=0.25, h_factor=0.25,
metadata=m, bboxes=bb, bbox_format="pascal_voc")),
("meme_format", lambda im, m, bb: imaugs.meme_format(im, text="BOXED", caption_height=80,
metadata=m, bboxes=bb,
bbox_format="pascal_voc")),
]
def draw_box(img, box, colour=(0, 255, 0)):
out = img.copy().convert("RGB")
ImageDraw.Draw(out).rectangle([float(v) for v in box], outline=colour, width=4)
return out
bbox_panels = [("original", draw_box(DEMO_IMG, DEMO_BOX))]
for label, op in BBOX_OPS:
m = []
try:
out = op(DEMO_IMG, m, [DEMO_BOX])
dst = m[0]["dst_bboxes"][0]
bbox_panels.append((f"{label}\n{tuple(round(v) for v in dst)}", draw_box(out, dst)))
print(f" {label:12s} {DEMO_BOX} -> {tuple(round(v, 1) for v in dst)}")
except Exception as e:
print(f" [skip] {label}: {type(e).__name__}: {e}")
show_grid(bbox_panels, cols=3, title="§4 Boxes follow the pixels automatically")
print("\n### §5 CUSTOM TRANSFORMS " + "#" * 51)
class RecompressionChain(ImageBaseTransform):
"""Simulate an image surviving N rounds of platform re-encoding.
Subclassing BaseTransform (rather than using ApplyLambda) buys you: the `p`
probability gate, `force=True`, and full participation in Compose/OneOf.
"""
def __init__(self, n_rounds: int = 3, min_q: int = 12, max_q: int = 45,
downscale: float = 0.85, p: float = 1.0):
super().__init__(p)
self.n_rounds, self.min_q, self.max_q, self.downscale = n_rounds, min_q, max_q, downscale
def apply_transform(self, image, metadata=None, bboxes=None, bbox_format=None):
src_w, src_h = image.size
out, qualities = image, []
for _ in range(self.n_rounds):
q = random.randint(self.min_q, self.max_q)
qualities.append(q)
out = imaugs.encoding_quality(out, quality=q)
out = imaugs.scale(out, factor=self.downscale)
out = out.resize((src_w, src_h), Image.BILINEAR)
if metadata is not None:
metadata.append({
"name": "recompression_chain",
"src_width": src_w, "src_height": src_h,
"dst_width": out.size[0], "dst_height": out.size[1],
"n_rounds": self.n_rounds, "qualities": qualities,
"intensity": float(100 * (1 - np.mean(qualities) / 100)),
})
return out
vignette = imaugs.ApplyLambda(aug_function=lambda im: Image.composite(
im, Image.new("RGB", im.size, (0, 0, 0)),
Image.radial_gradient("L").resize(im.size).point(lambda v: 255 - v)))
random.seed(SEED)
custom_meta = []
show_grid([
("original", DEMO_IMG),
("RecompressionChain(n=3)", RecompressionChain(n_rounds=3)(DEMO_IMG, metadata=custom_meta)),
("RecompressionChain(n=6)", RecompressionChain(n_rounds=6, min_q=5, max_q=20)(DEMO_IMG)),
("ApplyLambda vignette", vignette(DEMO_IMG)),
], cols=4, title="§5 Custom transforms drop straight into the AugLy API")
print(" custom metadata:", custom_meta[0])
CUSTOM_PIPELINE = imaugs.Compose([RecompressionChain(n_rounds=2, p=1.0),
imaugs.RandomEmojiOverlay(p=1.0)])
_ = CUSTOM_PIPELINE(DEMO_IMG)
print(" composed with built-ins: OK")
We construct probabilistic augmentation pipelines with Compose and OneOf while controlling reproducibility through explicit random seeds. We demonstrate how AugLy automatically propagates bounding-box coordinates through spatial transformations. We then implement a custom BaseTransform and combine it with built-in AugLy transforms.
print("\n### §6 COPY-DETECTION ROBUSTNESS BENCHMARK " + "#" * 33)
from scipy.fftpack import dct
def phash(img: Image.Image, hash_size: int = 8, highfreq: int = 4) -> np.ndarray:
"""Classic DCT perceptual hash -> 64-bit signature as a bool array."""
size = hash_size * highfreq
px = np.asarray(img.convert("L").resize((size, size), Image.LANCZOS), dtype=np.float64)
d = dct(dct(px, axis=0, norm="ortho"), axis=1, norm="ortho")[:hash_size, :hash_size]
return (d > np.median(d[1:, 1:])).ravel()
def hamming(a, b) -> int:
return int(np.count_nonzero(a != b))
INDEX = np.stack([phash(im) for im in IMAGES])
ATTACKS = {
"brightness x1.6": lambda im: imaugs.brightness(im, factor=1.6),
"blur r=3": lambda im: imaugs.blur(im, radius=3.0),
"jpeg q=8": lambda im: imaugs.encoding_quality(im, quality=8),
"crop 80%": lambda im: imaugs.crop(im, x1=.1, y1=.1, x2=.9, y2=.9),
"rotate 12": lambda im: imaugs.rotate(im, degrees=12),
"hflip": lambda im: imaugs.hflip(im),
"grayscale": lambda im: imaugs.grayscale(im),
"pixelize 0.2": lambda im: imaugs.pixelization(im, ratio=0.2),
"noise var=.03": lambda im: imaugs.random_noise(im, var=0.03),
"emoji overlay": lambda im: imaugs.overlay_emoji(im, emoji_size=0.35, opacity=0.9),
"meme format": lambda im: imaugs.meme_format(im, text="LOL", caption_height=70),
"screenshot": lambda im: imaugs.overlay_onto_screenshot(im),
"perspective s=40": lambda im: imaugs.perspective_transform(im, sigma=40.0),
"scale 0.35": lambda im: imaugs.scale(im, factor=0.35),
"stripes": lambda im: imaugs.overlay_stripes(im, line_width=0.4, line_opacity=0.7),
"re-encode chain": lambda im: RecompressionChain(n_rounds=3)(im),
"REUPLOAD pipeline": lambda im: REUPLOAD_PIPELINE(im),
}
rows = []
for attack, fn in ATTACKS.items():
random.seed(SEED)
np.random.seed(SEED)
hits, dists, failures = 0, [], 0
for i, im in enumerate(IMAGES):
try:
q = phash(fn(im))
except Exception:
failures += 1
continue
d = np.array([hamming(q, h) for h in INDEX])
hits += int(d.argmin() == i)
dists.append(int(d[i]))
n = len(IMAGES) - failures
rows.append({"attack": attack,
"top1_recall": hits / max(n, 1),
"mean_hamming": float(np.mean(dists)) if dists else np.nan,
"errors": failures})
bench = pd.DataFrame(rows).sort_values("top1_recall")
print(bench.to_string(index=False, float_format=lambda v: f"{v:.3f}"))
fig, ax = plt.subplots(1, 2, figsize=(14, 6))
colours = ["#c0392b" if r < .5 else "#e67e22" if r < .9 else "#27ae60"
for r in bench.top1_recall]
ax[0].barh(bench.attack, bench.top1_recall, color=colours)
ax[0].set_xlabel("top-1 retrieval recall"); ax[0].set_xlim(0, 1.05)
ax[0].axvline(0.9, ls="--", c="k", lw=1)
ax[0].set_title("pHash survival per AugLy attack")
ax[1].scatter(bench.mean_hamming, bench.top1_recall, s=70, c=colours)
for _, r in bench.iterrows():
ax[1].annotate(r.attack, (r.mean_hamming, r.top1_recall), fontsize=7,
xytext=(3, 3), textcoords="offset points")
ax[1].set_xlabel("mean Hamming distance to the true match (0-64)")
ax[1].set_ylabel("top-1 recall")
ax[1].set_title("Distortion vs. retrieval failure")
plt.tight_layout(); plt.show()
worst = bench.head(3).attack.tolist()
print(f"\n pHash breaks under: {worst}")
print(" -> exactly the augmentations you'd add to training, or handle with a")
print(" geometry-invariant embedding instead of a hash.")
We build a perceptual-hash index over the synthetic image corpus and evaluate its robustness against a broad collection of AugLy distortions. We measure top-1 retrieval recall and Hamming distance for every attack to quantify how different transformations affect copy detection. We visualize the results to identify the augmentations that most strongly degrade perceptual matching.
print("\n### §7 TEXT ATTACK / DEFEND / HARDEN " + "#" * 39)
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.pipeline import make_pipeline
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
X_tr, X_te, y_tr, y_te = train_test_split(TEXTS, LABELS, test_size=0.3,
random_state=SEED, stratify=LABELS)
def new_model():
return make_pipeline(
TfidfVectorizer(analyzer="word", ngram_range=(1, 2), sublinear_tf=True),
LogisticRegression(max_iter=1000, C=4.0),
)
baseline = new_model().fit(X_tr, y_tr)
clean_acc = accuracy_score(y_te, baseline.predict(X_te))
print(f"clean test accuracy: {clean_acc:.3f}\n")
ATTACK_SUITE = {
"typos (keyboard+misspell)": textaugs.SimulateTypos(aug_word_p=0.45, typo_type="all"),
"unicode homoglyphs": textaugs.ReplaceSimilarUnicodeChars(aug_word_p=0.8, aug_char_p=0.4),
"leetspeak lookalikes": textaugs.ReplaceSimilarChars(aug_word_p=0.8, aug_char_p=0.4),
"zero-width injection": textaugs.InsertZeroWidthChars(granularity="word", cadence=2.0),
"punctuation injection": textaugs.InsertPunctuationChars(granularity="word", cadence=2.0,
vary_chars=True),
"whitespace injection": textaugs.InsertWhitespaceChars(granularity="word", cadence=3.0),
"fun fonts": textaugs.ReplaceFunFonts(aug_p=0.8, granularity="word",
vary_fonts=True),
"upside down": textaugs.ReplaceUpsideDown(aug_p=0.6, granularity="word"),
"bidirectional": textaugs.ReplaceBidirectional(granularity="word"),
"split words": textaugs.SplitWords(aug_word_p=0.5),
"merge words": textaugs.MergeWords(aug_word_p=0.5),
"CaSe ChAoS": textaugs.ChangeCase(granularity="word", cadence=2.0, case="upper"),
}
print("what the attacks look like on one sentence:")
for name, aug in ATTACK_SUITE.items():
random.seed(SEED)
print(f" {name:26s} {as_str(aug(DEMO_TEXT))[:72]}")
ZERO_WIDTH = dict.fromkeys(
[0x200B, 0x200C, 0x200D, 0x2060, 0x2061, 0x2062, 0x2063, 0x2064, 0xFEFF,
0x202A, 0x202B, 0x202C, 0x202D, 0x202E, 0x2066, 0x2067, 0x2068, 0x2069, 0x200E, 0x200F]
)
PUNCT_TABLE = str.maketrans("", "", "".join(c for c in string.punctuation if c not in "'"))
def sanitize(text: str) -> str:
"""Cheap, high-yield preprocessing against Unicode-obfuscation attacks."""
t = text.translate(ZERO_WIDTH)
t = unicodedata.normalize("NFKD", t)
t = "".join(ch for ch in t if not unicodedata.combining(ch))
t = t.translate(PUNCT_TABLE)
return " ".join(t.lower().split())
random.seed(SEED)
TRAIN_AUGS = [ATTACK_SUITE[k] for k in
["typos (keyboard+misspell)", "unicode homoglyphs", "leetspeak lookalikes",
"zero-width injection", "punctuation injection", "fun fonts", "CaSe ChAoS"]]
aug_X, aug_y = list(X_tr), list(y_tr)
for aug in TRAIN_AUGS:
for x, y in zip(X_tr, y_tr):
aug_X.append(as_str(aug(x)))
aug_y.append(y)
print(f"\ntrain set: {len(X_tr)} -> {len(aug_X)} examples after AugLy adversarial training")
hardened = make_pipeline(
TfidfVectorizer(analyzer="char_wb", ngram_range=(3, 5), sublinear_tf=True,
preprocessor=sanitize),
LogisticRegression(max_iter=2000, C=4.0),
).fit(aug_X, aug_y)
results = []
for name, aug in ATTACK_SUITE.items():
random.seed(SEED)
X_atk = [as_str(aug(x)) for x in X_te]
results.append({
"attack": name,
"baseline": accuracy_score(y_te, baseline.predict(X_atk)),
"+ sanitize": accuracy_score(y_te, baseline.predict([sanitize(x) for x in X_atk])),
"+ sanitize + adv-train": accuracy_score(y_te, hardened.predict(X_atk)),
})
res = pd.DataFrame(results).set_index("attack").sort_values("baseline")
res.loc["-- CLEAN --"] = [clean_acc,
accuracy_score(y_te, baseline.predict([sanitize(x) for x in X_te])),
accuracy_score(y_te, hardened.predict(X_te))]
print("\n" + res.to_string(float_format=lambda v: f"{v:.3f}"))
print(f"\nmean accuracy under attack: baseline {res.iloc[:-1,0].mean():.3f}"
f" -> sanitized {res.iloc[:-1,1].mean():.3f}"
f" -> hardened {res.iloc[:-1,2].mean():.3f}")
plot_df = res.drop(index="-- CLEAN --")
ax = plot_df.plot.barh(figsize=(11, 7), width=0.78,
color=["#c0392b", "#e6a817", "#27ae60"])
ax.axvline(clean_acc, ls="--", c="k", lw=1.2, label=f"clean acc = {clean_acc:.2f}")
ax.set_xlabel("accuracy under attack"); ax.set_xlim(0, 1.05)
ax.set_title("§7 AugLy as a red-team suite — and as the fix")
ax.legend(loc="lower right", fontsize=8)
plt.tight_layout(); plt.show()
We create a text classification baseline and systematically expose it to typos, Unicode homoglyphs, invisible characters, punctuation changes, and other adversarial transformations. We implement Unicode normalization and sanitization to remove several classes of obfuscation. We then use AugLy-generated adversarial examples during training and compare the resulting hardened model against the baseline.
print("\n### §8 AUDIO AUGMENTATION " + "#" * 50)
try:
import augly.audio as audaugs
AUDIO_ZOO = {
"pitch_shift +4": lambda a, sr, m: audaugs.pitch_shift(a, sr, n_steps=4.0, metadata=m),
"time_stretch 1.5x": lambda a, sr, m: audaugs.time_stretch(a, sr, rate=1.5, metadata=m),
"change_volume -12dB":lambda a, sr, m: audaugs.change_volume(a, sr, volume_db=-12.0, metadata=m),
"clicks": lambda a, sr, m: audaugs.clicks(a, sr, seconds_between_clicks=0.25,
metadata=m),
"background_noise": lambda a, sr, m: audaugs.add_background_noise(a, sr, snr_level_db=5.0,
metadata=m),
"low_pass 1kHz": lambda a, sr, m: audaugs.low_pass_filter(a, sr, cutoff_hz=1000.0,
metadata=m),
"high_pass 2kHz": lambda a, sr, m: audaugs.high_pass_filter(a, sr, cutoff_hz=2000.0,
metadata=m),
"peaking_eq": lambda a, sr, m: audaugs.peaking_equalizer(a, sr, center_hz=800.0,
gain_db=-12.0, metadata=m),
"harmonic": lambda a, sr, m: audaugs.harmonic(a, sr, metadata=m),
"percussive": lambda a, sr, m: audaugs.percussive(a, sr, metadata=m),
"clip (50%)": lambda a, sr, m: audaugs.clip(a, sr, duration_factor=0.5, metadata=m),
"loop x2": lambda a, sr, m: audaugs.loop(a, sr, n=1, metadata=m),
"normalize": lambda a, sr, m: audaugs.normalize(a, sr, metadata=m),
"speed 1.4x": lambda a, sr, m: audaugs.speed(a, sr, factor=1.4, metadata=m),
"tempo 0.8x": lambda a, sr, m: audaugs.tempo(a, sr, factor=0.8, metadata=m),
"reverb": lambda a, sr, m: audaugs.reverb(a, sr, reverberance=80.0, metadata=m),
}
ok, audio_meta = [], []
for name, fn in AUDIO_ZOO.items():
m = []
try:
out, out_sr = fn(AUDIO.copy(), SR, m)
ok.append((name, np.asarray(out).squeeze(), out_sr))
audio_meta.append({"name": m[0]["name"], "intensity": m[0].get("intensity"),
"dst_duration": np.asarray(out).squeeze().shape[-1] / out_sr})
except Exception as e:
print(f" [skip] {name}: {type(e).__name__}: {str(e)[:90]}")
print("\n" + pd.DataFrame(audio_meta).to_string(index=False,
float_format=lambda v: f"{v:.3f}"))
n = min(len(ok), 8)
fig, axes = plt.subplots(n + 1, 1, figsize=(11, 1.5 * (n + 1)), sharex=False)
axes[0].plot(AUDIO, lw=.5, color="k"); axes[0].set_ylabel("orig", fontsize=7)
for ax, (name, sig, _) in zip(axes[1:], ok[:n]):
ax.plot(sig, lw=.5); ax.set_ylabel(name, fontsize=6, rotation=0, ha="right", va="center")
for ax in axes:
ax.set_xticks([]); ax.set_yticks([])
fig.suptitle("§8 Audio waveforms after AugLy augmentation")
plt.tight_layout(); plt.show()
try:
from IPython.display import Audio, display
print("original:"); display(Audio(AUDIO, rate=SR))
for name, sig, sr_out in ok[:3]:
print(name); display(Audio(sig, rate=sr_out))
except Exception:
pass
except ImportError as e:
print(f" audio module unavailable ({e}).")
print(" On Colab librosa/torch/torchaudio are preinstalled; elsewhere run:")
print(' pip install "librosa>=0.8.1" soundfile audioread torch torchaudio')
print("\n### §9 METADATA WAREHOUSE " + "#" * 50)
warehouse = []
random.seed(SEED)
for idx, im in enumerate(IMAGES[:8]):
for name, fn in IMAGE_ZOO.items():
meta = []
try:
out = fn(im, meta)
except Exception:
continue
m = meta[0]
warehouse.append({
"source_id": idx,
"augmentation": m["name"],
"intensity": m.get("intensity"),
"src_w": m.get("src_width"), "src_h": m.get("src_height"),
"dst_w": m.get("dst_width"), "dst_h": m.get("dst_height"),
"area_ratio": (m.get("dst_width", 0) * m.get("dst_height", 0)) /
max(m.get("src_width", 1) * m.get("src_height", 1), 1),
})
wh = pd.DataFrame(warehouse)
print(wh.head(8).to_string(index=False, float_format=lambda v: f"{v:.2f}"))
print(f"\nlogged {len(wh)} augmented samples from {wh.source_id.nunique()} sources")
print("\nhardest augmentations by mean intensity:")
print(wh.groupby("augmentation").intensity.mean().sort_values(ascending=False)
.head(8).to_string(float_format=lambda v: f"{v:.1f}"))
wh.to_csv("augly_metadata.csv", index=False)
print("\nwrote augly_metadata.csv (join this to your training manifest)")
We extend the augmentation workflow to audio by applying transformations such as pitch shifting, time stretching, filtering, noise injection, and reverb while gracefully skipping unavailable dependencies. We inspect the resulting waveforms and, where supported, play augmented samples directly in Colab. We also build a metadata warehouse that records augmentation type, intensity, dimensions, and area changes for downstream analysis.
print("\n### §10 PYTORCH DATASET / DATALOADER " + "#" * 39)
try:
import torch
from torch.utils.data import Dataset, DataLoader
import torchvision.transforms as T
class AugLyDataset(Dataset):
"""AugLy transforms are drop-in for torchvision because they are PIL->PIL."""
def __init__(self, images, labels, train=True):
self.images, self.labels = images, labels
aug = imaugs.Compose([
imaugs.OneOf([imaugs.RandomBlur(min_radius=0.5, max_radius=2.5),
imaugs.RandomPixelization(min_ratio=0.3, max_ratio=1.0),
imaugs.EncodingQuality(quality=20)], p=0.8),
imaugs.RandomBrightness(min_factor=0.7, max_factor=1.4, p=0.6),
imaugs.RandomEmojiOverlay(p=0.3),
RecompressionChain(n_rounds=1, p=0.3),
])
steps = ([aug] if train else []) + [
T.Resize((128, 128)),
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
self.tf = T.Compose(steps)
def __len__(self):
return len(self.images)
def __getitem__(self, i):
return self.tf(self.images[i]), self.labels[i]
ds = AugLyDataset(IMAGES, [i % 3 for i in range(len(IMAGES))], train=True)
dl = DataLoader(ds, batch_size=8, shuffle=True, num_workers=0)
xb, yb = next(iter(dl))
print(f" batch tensor {tuple(xb.shape)} dtype={xb.dtype} labels={yb.tolist()}")
denorm = (xb[:8] * torch.tensor([0.229, 0.224, 0.225]).view(3, 1, 1)
+ torch.tensor([0.485, 0.456, 0.406]).view(3, 1, 1)).clamp(0, 1)
show_grid([(f"batch[{i}] y={yb[i].item()}", denorm[i].permute(1, 2, 0).numpy())
for i in range(min(8, len(denorm)))],
cols=4, title="§10 One augmented batch straight out of the DataLoader")
np_img = np.asarray(DEMO_IMG)
np_out = imaugs.aug_np_wrapper(np_img, imaugs.overlay_emoji,
**{"opacity": 0.8, "y_pos": 0.4})
print(f" aug_np_wrapper: {np_img.shape} -> {np_out.shape} ({np_out.dtype})")
except ImportError:
print(" torch/torchvision not installed — skipping (Colab has them by default).")
print("\n" + "=" * 78)
print("DONE. Ideas from here:")
print(" * swap pHash in §6 for a real embedding (CLIP / DINOv2) and re-run the table")
print(" * feed §9's CSV into a curriculum: train on low-intensity augs first")
print(" * add augly.video (pip install 'augly' + apt install ffmpeg) and")
print(" benchmark frame-level robustness the same way")
print("=" * 78)
We integrate AugLy directly into a PyTorch Dataset and DataLoader, allowing augmentations to run as part of the training-time preprocessing pipeline. We apply normalization and tensor conversion after augmentation and visualize a generated training batch to verify the complete data path. We also demonstrate AugLy’s NumPy-native wrapper and summarize practical extensions for embedding-based and video robustness benchmarks.
In conclusion, we showed how to use AugLy as more than a collection of independent augmentation functions by treating it as a systematic framework for robustness engineering. We measured how different image transformations affect copy-detection retrieval, show how adversarial text transformations expose weaknesses in conventional classifiers, and evaluate sanitization and adversarial training as complementary defenses. We also preserved augmentation metadata and intensity information so every generated sample remains traceable and analyzable, while custom transforms let us model application-specific distortions. By integrating image, text, audio, and PyTorch workflows within one reproducible pipeline, we established a foundation for building augmentation-aware training systems, robustness benchmarks, and production data pipelines.
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Sana Hassan
Sana Hassan, a consulting intern at Marktechpost and dual-degree student at IIT Madras, is passionate about applying technology and AI to address real-world challenges. With a keen interest in solving practical problems, he brings a fresh perspective to the intersection of AI and real-life solutions.